Compound specific isotope analysis of individual amino acids in reef corals
CB Wall, NJ Wallsgrove, RD Gates, BN Popp (Limnology and Oceanography)
January 2021
if (!require('knitr')) install.packages('knitr'); library('knitr')
knitr::opts_chunk$set(warning=FALSE, message=FALSE, fig.align='center')
###############
# Carbon and Nitrogen Isotopic Analysis of Individual Amino Acids in Montipora capitata
# Author: C. Wall
# Collaborators: Brian Popp, Ruth Gates, Natalie Wallsgrove
# Institution: University of Hawai'i at Mānoa
###############
# Load in packages
if (!require("pacman")) install.packages("pacman"); library(pacman) # for rapid install if not in library
pacman::p_load(devtools, ellipse, ggbiplot, vqv, patchwork, graphics, plyr, effects, MASS, tidyverse, dplyr, plotrix, vegan, cowplot, caret, reshape, car, emmeans, lmerTest)Background
Compound specific isotope analysis (CSIA) of individual amino acids (AA) have been developed to better understand ecosystem food webs, trophic positions, and sources of nutrition in biological samples ranging from bacteria to cetaceans. Bulk tissue isotope analysis requires separate accounting for isotopic signatures at the base of the food web, which vary in across locations and time periods. However, CSIA can account for both source and trophic isotope effects in a single sample of a consumer’s tissue. In reef coral, advances in AA-CSIA/isotope analysis and so called “isotope fingerprinting” may help advance the study of coral trophic plasticity.
Here, we exposed a reef coral (Montipora capitata) in symbiosis with Symbiodiniaceae algae to experimental treatments (autotrophy, mixotrophy, heterotrophy) to test for trophic shifts and amino acid (AA) sources using paired bulk (δ13C, δ15N) and AA-CSIA (δ13CAA, δ15NAA).
AA grouping
Esstential amino acids (EAA) refer to AA-carbon, these are a group of amino acids that organisms require through their diet and show limited change across trophic levels.
Non-essential amino acids (NEAA) refer to AA-carbon, there amino acids are synthesized from carbon pools or direct routing of nonessential amino acids sourced from dietary protein.
Source amino acids refer to AA-nitrogen, with a group of AA that exhibit little change in isotopic composition with increasing trophic levels and reflect the isotopic composition of the ‘source material’ at the base of the food web from which they originated.
Trophic amino acids refer to AA-nitrogen, and are a group of AA that show significant 15N enrichment compared to source-AA, which correspond to trophic steps.This enrichment is quite large and may exceed 8 ‰.
Bulk isotopes
Bulk sample isotope data from plankton in Kāne’ohe Bay (previously published, Wall et al. 2020, ISME J)
SWiso<-read.csv("data/other/Wall_bulk_isotopes_SW_ismej.csv")
# fraction matters most
#### d13C
mod1<-(lm(d13C~Location+Season+SW.fraction..um, data=SWiso)); Anova(mod1, type=2) # no location or season effect## Anova Table (Type II tests)
##
## Response: d13C
## Sum Sq Df F value Pr(>F)
## Location 17.926 5 1.3418 0.2626013
## Season 7.921 1 2.9645 0.0914188 .
## SW.fraction..um 76.419 4 7.1504 0.0001286 ***
## Residuals 130.920 49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1posthoc<-emmeans(mod1, ~SW.fraction..um)
multcomp::cld(posthoc, Letters=letters) # only diff for the 10-100 um fraction## SW.fraction..um emmean SE df lower.CL upper.CL .group
## 100-243 -21.1 0.472 49 -22.0 -20.1 a
## 0-10 -21.0 0.472 49 -22.0 -20.1 a
## >243 -20.8 0.472 49 -21.7 -19.8 a
## <243 -20.4 0.472 49 -21.4 -19.5 a
## 10-100 -18.1 0.472 49 -19.0 -17.1 b
##
## Results are averaged over the levels of: Location, Season
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 5 estimates
## significance level used: alpha = 0.05#### d15N
mod2<-(lm(d15N~Location+Season+SW.fraction..um, data=SWiso)); Anova(mod2, type=2) # mild season effect## Anova Table (Type II tests)
##
## Response: d15N
## Sum Sq Df F value Pr(>F)
## Location 2.3255 5 1.7291 0.14557
## Season 1.0935 1 4.0653 0.04927 *
## SW.fraction..um 30.3773 4 28.2335 3.454e-12 ***
## Residuals 13.1802 49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1posthoc<-emmeans(mod2, ~SW.fraction..um)
multcomp::cld(posthoc, Letters=letters)## SW.fraction..um emmean SE df lower.CL upper.CL .group
## 0-10 5.33 0.15 49 5.03 5.63 a
## <243 5.92 0.15 49 5.62 6.22 ab
## 100-243 6.50 0.15 49 6.20 6.80 bc
## >243 6.77 0.15 49 6.47 7.07 c
## 10-100 7.41 0.15 49 7.11 7.71 d
##
## Results are averaged over the levels of: Location, Season
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 5 estimates
## significance level used: alpha = 0.05posthoc<-emmeans(mod2, ~Season)
multcomp::cld(posthoc, Letters=letters) # diff small <0.3 permil## Season emmean SE df lower.CL upper.CL .group
## summer 6.25 0.0947 49 6.06 6.44 a
## winter 6.52 0.0947 49 6.33 6.71 b
##
## Results are averaged over the levels of: Location, SW.fraction..um
## Confidence level used: 0.95
## significance level used: alpha = 0.05########
SWiso$Reef<-factor(SWiso$Reef, levels=c("F1-46", "R42", "F2-R25", "R25", "F8-R10", "HIMB"))
SWiso$Location<-factor(SWiso$Location, levels=c("NW", "NE", "CW", "CE", "SW", "SE"))
SWiso$SW.fraction..um<-factor(SWiso$SW.fraction..um, levels=c(">243", "<243", "100-243", "10-100", "0-10"))
winter.data<-SWiso[(SWiso$Season=="winter"),]
summer.data<-SWiso[(SWiso$Season=="summer"),]
#### making scatter for d15N and d13C, pooled across seasons and sites
mix.N.mean<-aggregate(d15N~SW.fraction..um, data=SWiso, mean)
mix.N.se<-aggregate(d15N~SW.fraction..um, data=SWiso, std.error)
mix.N.n<-aggregate(d15N~SW.fraction..um, data=SWiso, length)
mix.C.mean<-aggregate(d13C~SW.fraction..um, data=SWiso, mean)
mix.C.se<-aggregate(d13C~SW.fraction..um, data=SWiso, std.error)
mix.C.n<-aggregate(d13C~SW.fraction..um, data=SWiso, length)
mix.data<-cbind(mix.N.mean, mix.C.mean[c(2,0)], mix.N.se[c(2,0)], mix.C.se[c(2,0)]); colnames(mix.data)=c("fraction", "d15N", "d13C", "d15N.se", "d13C.se")
write.table(mix.data, file = "output/bulk isotopes.Wall.ISMEJ.csv", sep = ",", quote = FALSE, row.names = F)
Figure NA. Stable isotope C and N in plankton samples by size fraction from summer and winter 2016 at six sampling locations.
Carbon and nitrogen isotope values in plankton, Symbiodiniaceae symbionts and coral host tissues. With linear models testing effect of treatment and fraction
bulk<-read.csv("data/bulkCN.isotopes.csv")
bulk$Treat.Int<-factor(bulk$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
bulk$Fraction<-factor(bulk$Fraction, levels=c("host", "symb", "plank"))
bulk.HS<-bulk[!(bulk$Fraction=="plank"),] # plankton removed from fraction
bulk.d15N.means<-aggregate(d15N~Fraction+Treat.Int, data=bulk, mean, na.rm=TRUE); bulk.d15N.means## Fraction Treat.Int d15N
## 1 host L-NF 5.35
## 2 symb L-NF 4.20
## 3 host L-F 5.35
## 4 symb L-F 4.05
## 5 host D-F 5.45
## 6 symb D-F 4.40
## 7 plank plank 7.10bulk.d13C.means<-aggregate(d13C~Fraction+Treat.Int, data=bulk, mean, na.rm=TRUE); bulk.d13C.means## Fraction Treat.Int d13C
## 1 host L-NF -16.60
## 2 symb L-NF -17.40
## 3 host L-F -16.55
## 4 symb L-F -17.60
## 5 host D-F -15.75
## 6 symb D-F -15.95
## 7 plank plank -21.80bulk.CN.means<-aggregate(C.N~Fraction+Treat.Int, data=bulk, mean, na.rm=TRUE); bulk.CN.means## Fraction Treat.Int C.N
## 1 host L-NF 6.201642
## 2 symb L-NF 6.711709
## 3 host L-F 6.259647
## 4 symb L-F 7.022590
## 5 host D-F 5.973318
## 6 symb D-F 5.328831
## 7 plank plank 4.418660bulk.means<-cbind(bulk.d15N.means, bulk.d13C.means[3], bulk.CN.means[3])
### fig formatting
format.fig<-
theme(axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")),
axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(text = element_text(size=8)) +
theme(legend.text=element_text(size=10), legend.key = element_blank()) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black", size=0.5))
##### bulk figures
d13C.m<-aggregate(d13C~Treat.Int, data=bulk, mean, na.rm=TRUE)
d13C.n<-aggregate(d13C~Treat.Int, data=bulk, length)
# d13C
d13C.plot<-ggplot(bulk, aes(y=d13C, x=Treat.Int))+
geom_boxplot(aes(fill=Fraction)) +
geom_dotplot(aes(fill = Fraction, color = Fraction),
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(delta^{13}, C, " (\u2030, V-PDB)")))+
xlab("Treatment") + theme(legend.title = element_blank()) +
format.fig
anova(lm(d13C ~ Fraction+Treat.Int, data=bulk.HS))## Analysis of Variance Table
##
## Response: d13C
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.4008 1.40083 1.5317 0.2510
## Treat.Int 2 3.7717 1.88583 2.0620 0.1896
## Residuals 8 7.3167 0.91458# d15N
d15N.plot<-ggplot(bulk, aes(y=d15N, x=Treat.Int))+
geom_boxplot(aes(fill=Fraction)) +
geom_dotplot(aes(fill = Fraction, color = Fraction),
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(delta^{15}, N, " (\u2030, air)")))+
xlab("Treatment") +
format.fig + theme(legend.position = "none")
anova(lm(d15N ~ Fraction+Treat.Int, data=bulk.HS))## Analysis of Variance Table
##
## Response: d15N
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 4.0833 4.0833 14.7702 0.004925 **
## Treat.Int 2 0.1050 0.0525 0.1899 0.830662
## Residuals 8 2.2117 0.2765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1####
# C:N
C.N.plot<-ggplot(bulk, aes(y=C.N, x=Treat.Int), na.rm=T)+
geom_boxplot(aes(fill=Fraction)) +
geom_dotplot(aes(fill = Fraction, color = Fraction),
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(C:N)))+
xlab("Treatment") +
format.fig + theme(legend.position = "none")
anova(lm(C.N ~ Fraction+Treat.Int, data=bulk.HS))## Analysis of Variance Table
##
## Response: C.N
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.13168 0.13168 0.7228 0.41995
## Treat.Int 2 2.21760 1.10880 6.0861 0.02474 *
## Residuals 8 1.45749 0.18219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1####
# d13H-S
d13C.HS.plot<-ggplot(bulk.HS, aes(y=d13C.H.S, x=Treat.Int), na.rm=T)+
geom_boxplot() +
geom_dotplot(
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(delta^{13}, C[H-S], " (\u2030)"))) +
xlab("Treatment") +
format.fig + theme(legend.position = "none")
anova(lm(d13C.H.S ~ Treat.Int, data=bulk.HS))## Analysis of Variance Table
##
## Response: d13C.H.S
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.76333 0.38167 7.8966 0.06378 .
## Residuals 3 0.14500 0.04833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# d15NH-S
d15N.HS.plot<-ggplot(bulk.HS, aes(y=d15N.H.S, x=Treat.Int), na.rm=T)+
geom_boxplot() +
geom_dotplot(
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(delta^{15}, N[H-S], " (\u2030)")))+
xlab("Treatment") +
format.fig + theme(legend.position = "none")
anova(lm(d15N.H.S ~ Treat.Int, data=bulk.HS))## Analysis of Variance Table
##
## Response: d15N.H.S
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.06333 0.031667 0.2879 0.7685
## Residuals 3 0.33000 0.110000####
bulk.legend <- get_legend(
# create some space to the left of the legend
d13C.plot + theme(legend.box.margin = margin(0, 0, 0, 12)))
bulk.figures<-(d13C.plot+theme(legend.position = "none") | d15N.plot | C.N.plot | d13C.HS.plot | d15N.HS.plot |
bulk.legend)
print(bulk.figures)
Figure 1. Bulk tissue carbon and nitrogen isotope values for corals (host), their endosymbiont Symbiodiniaceae (symbiont), and a pooled plankton sample (plank) at three Light-by-Feeding nutrition treatments. L-NF (Light-Not Fed, autotrophic), L-F (Light-Fed, mixotrophic), Dark-Fed (heterotrophic). Boxplots are n=2, except for plankton (n=1).
ggsave("figures/Fig 1. bulk isotope.pdf", height=3.5, width=12, encod="MacRoman")CSIA Carbon and Nitrogen
Carbon in amino acids of plankton, Symbiodiniaceae symbionts and coral host tissues.
######## ########
## Carbon
######## ########
rm(list=ls())
d13C.dat<-read.csv("data/d13C.CSIA.wide.csv") # wide form carbon data
colnames(d13C.dat)
d13C.dat$Fraction<-factor(d13C.dat$Fraction, levels=c("host", "symb", "plank"))
d13C.dat$Treat.Int<-factor(d13C.dat$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
d13C.dat<-d13C.dat[ , !(names(d13C.dat) %in% c("Norleucine", "Aminoadipic.Acid", "Methionine"))] #remove Norleucine, Methionine, Aminoadipic Acid
######## ########
## Nitrogen
######## ########
d15N.dat<-read.csv("data/d15N.CSIA.wide.csv") # wide form carbon data
d15N.dat<-d15N.dat[ , !(names(d15N.dat) %in% c("Norleucine", "Aminoadipic.Acid", "Methionine"))]
d15N.dat$Fraction<-factor(d15N.dat$Fraction, levels=c("host", "symb", "plank"))Permanova
## Permanova carbon
d13C.dat.perm<- d13C.dat[!(d13C.dat$Fraction=="plank"),]
df.manova.C<-d13C.dat.perm[, c(11:23)] # remove factor columns
df.manova.C.abs<-abs(df.manova.C) # change all to absolute values
set.seed(138)
perman.C<-adonis2(df.manova.C.abs~Fraction*Treat.Int, data=d13C.dat.perm, permutations=1000,
method="euclidean", sqrt.dist = TRUE)
perman.C## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## adonis2(formula = df.manova.C.abs ~ Fraction * Treat.Int, data = d13C.dat.perm, permutations = 1000, method = "euclidean", sqrt.dist = TRUE)
## Df SumOfSqs R2 F Pr(>F)
## Fraction 1 6.931 0.15293 1.7236 0.07493 .
## Treat.Int 2 7.895 0.17420 0.9817 0.46853
## Fraction:Treat.Int 2 6.369 0.14052 0.7919 0.74525
## Residual 6 24.128 0.53235
## Total 11 45.323 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1write.csv(perman.C, "output/perman.C.csv")
## Permanova nitrogen
d15N.dat.perm<- d15N.dat[!(d15N.dat$Fraction=="plank"),]
d15N.dat.perm$Threonine<-d15N.dat.perm$Threonine+3 # adding a constant to make positive values
d15N.dat.perm$Phenylalanine<-d15N.dat.perm$Phenylalanine+3 # adding a constant to make positive values
df.manova.N<-d15N.dat.perm[, c(11:23)] # remove factor columns
set.seed(138)
perman.N<-adonis2(df.manova.N~Fraction*Treat.Int, data=d15N.dat.perm, permutations=1000,
method="euclidean", sqrt.dist = TRUE)
perman.N## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## adonis2(formula = df.manova.N ~ Fraction * Treat.Int, data = d15N.dat.perm, permutations = 1000, method = "euclidean", sqrt.dist = TRUE)
## Df SumOfSqs R2 F Pr(>F)
## Fraction 1 7.065 0.18165 2.0418 0.01499 *
## Treat.Int 2 4.753 0.12220 0.6868 0.92408
## Fraction:Treat.Int 2 6.315 0.16235 0.9124 0.60140
## Residual 6 20.763 0.53380
## Total 11 38.896 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1write.csv(perman.N, "output/perman.N.csv")PCA
Carbon by treatment
######## ######## ######## ########
######## ######## ######## ######## by treatment
# PCA dataframe
PCA.df.C<-d13C.dat[, c(7:8,11:23)]
PC.C<- prcomp(PCA.df.C[,c(-1:-2)], center = TRUE, scale= TRUE)
PC.C.summary<-summary(PC.C)
ev.C<-PC.C$sdev^2
newdat.C<-PC.C$x[,1:4] # 2 PCAs explain 81% of variance
#plot(PC, type="lines", main="PC.area eigenvalues")
## PC1 and PC2
trt.color<-c("lightskyblue", "darkgoldenrod1", "gray70", "mediumorchid")
PC.fig1.C.trt <- ggbiplot(PC.C, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df.C[,1], ellipse = TRUE, ellipse.prob=0.90,
circle = FALSE) +
scale_color_manual(values=trt.color)+
theme_classic() +
scale_x_continuous(breaks=pretty_breaks(n=5))+
coord_cartesian(xlim = c(-8, 8), ylim=c(-4, 4))+
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.8) +
annotate("text", x=6, y=3.6, size=5, label= expression(paste(delta^{13},C[AA])))
print(PC.fig1.C.trt)#ggsave("figures/carbon/PCA_d13C.trt.pdf", height=5, width=8, encod="MacRoman")Nitrogen by treatment
# PCA dataframe
PCA.df.N<-d15N.dat[, c(7:8,11:23)]
PC.N<- prcomp(PCA.df.N[, c(-1:-2)], center = TRUE, scale= TRUE)
PC.N.summary<-summary(PC.N)
ev.N<-PC.N$sdev^2
newdat.N<-PC.N$x[,1:4] # 2 PCAs explain 74% of variance
#plot(PC, type="lines", main="PC.area eigenvalues")
######################## treatments
## PC1 and PC2
PC.fig2.N.trt <- ggbiplot(PC.N, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df.N[,1], ellipse = TRUE, ellipse.prob=0.90,
circle = FALSE) +
scale_color_manual(values=trt.color)+
theme_classic() +
coord_cartesian(xlim = c(-8, 5), ylim=c(-6, 6)) +
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.8) +
annotate("text", x=4, y=5.5, size=5, label= expression(paste(delta^{15},N[AA])))
print(PC.fig2.N.trt)#ggsave("figures/nitrogen/PCA_d15N.trt.pdf", height=5, width=6, encod="MacRoman")Carbon PCA by fraction (plankton, host, symbiont)
######## ######## ######## ########
######## ######## ######## ######## by fraction
frac.color<- c("coral", "seagreen3", "mediumorchid")
PC.fig3.C.frac <- ggbiplot(PC.C, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df.C[,2], ellipse = TRUE, ellipse.prob=0.90,
circle = FALSE) +
scale_color_manual(values=frac.color)+
theme_classic() +
coord_cartesian(xlim = c(-8, 8), ylim=c(-4, 4)) +
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.8) +
annotate("text", x=6, y=3.6, size=5, label= expression(paste(delta^{13},C[AA])))
print(PC.fig3.C.frac)#ggsave("figures/carbon/PCA_d13C.frac.pdf", height=5, width=8, encod="MacRoman")Nitrogen PCA by fraction (plankton, host, symbiont)
######################### fractions
## PC1 and PC2
PC.fig4.N.frac <- ggbiplot(PC.N, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df.N[,2], ellipse = TRUE, ellipse.prob=0.90,
circle = FALSE) +
scale_color_manual(values=frac.color)+
theme_classic() +
coord_cartesian(xlim = c(-8, 5), ylim=c(-6, 6)) +
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.8) +
annotate("text", x=4, y=5.5, size=5, label= expression(paste(delta^{15},N[AA])))
print(PC.fig4.N.frac)#ggsave("figures/nitrogen/PCA_d15N.frac.pdf", height=5, width=8, encod="MacRoman")Figure 2– 4 panel PCA.combined
#### compile the 4 PCA ###
library("cowplot")
plot_grid(PC.fig3.C.frac, PC.fig4.N.frac, PC.fig1.C.trt, PC.fig2.N.trt, ncol = 2)
Figure 2. Principal component analysis of carbon (left) and nitrogen (right) isotope values of individual amino acids in corals, Symbiodiniaceae, and a pooled plankton sample in relation to tissue fraction (top) and treatments (bottom). Ellipses represent 90% standard deviation with arrows for individual amino acids being significant (p<0.05) correlation vectors.
ggsave("figures/Fig 2.PCAs.pdf", height=8, width=11, encod="MacRoman")
######Carbon AA
Models
Run models looking for effects of Fraction or the Treatment-Interaction (feeding/light).
d13C.dat2<-d13C.dat[!(d13C.dat$Fraction=="plank"),] #remove plankton for now
d13C.host<-d13C.dat[(d13C.dat$Fraction=="host"),] # just host
d13C.symb<-d13C.dat[(d13C.dat$Fraction=="symb"),] # just symbiont
for(i in c(11:23)){
Y=d13C.dat2[,i]
mod<-aov(Y~Fraction+Treat.Int, data=d13C.dat2)
print(anova(mod))
print(TukeyHSD(mod))
plot(allEffects(mod), ylab=colnames(d13C.dat2)[i], cex.axis=0.5, cex.lab=0.5)
}## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 7.9280 7.9280 4.8489 0.05881 .
## Treat.Int 2 2.3861 1.1931 0.7297 0.51157
## Residuals 8 13.0802 1.6350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -1.625629 -3.328026 0.07676902 0.0588088
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -1.0214364 -3.605031 1.562158 0.5238902
## D-F-L-NF -0.1756140 -2.759208 2.407980 0.9794709
## D-F-L-F 0.8458224 -1.737772 3.429417 0.6346704
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 43.514 43.514 16.281 0.003762 **
## Treat.Int 2 38.910 19.455 7.279 0.015818 *
## Residuals 8 21.382 2.673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -3.808515 -5.985115 -1.631916 0.0037621
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -2.323060 -5.626314 0.9801931 0.1719320
## D-F-L-NF 2.085575 -1.217679 5.3888282 0.2286943
## D-F-L-F 4.408635 1.105382 7.7118885 0.0126616
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 2.197 2.1971 0.4464 0.5228
## Treat.Int 2 5.526 2.7628 0.5614 0.5914
## Residuals 8 39.372 4.9215
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host 0.8557828 -2.097784 3.80935 0.5228408
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -1.6618056 -6.144201 2.820590 0.5632924
## D-F-L-NF -0.8014268 -5.283822 3.680969 0.8682426
## D-F-L-F 0.8603788 -3.622017 5.342774 0.8501124
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.5098 0.5098 0.1910 0.6736
## Treat.Int 2 13.3334 6.6667 2.4977 0.1436
## Residuals 8 21.3529 2.6691
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host 0.4122222 -1.762895 2.58734 0.6736481
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -1.363812 -4.6648158 1.937192 0.4961333
## D-F-L-NF 1.216790 -2.0842139 4.517794 0.5667464
## D-F-L-F 2.580602 -0.7204022 5.881606 0.1247308
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 3.0549 3.0549 1.2730 0.2919
## Treat.Int 2 4.4772 2.2386 0.9328 0.4324
## Residuals 8 19.1986 2.3998
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -1.009111 -3.071587 1.053365 0.291906
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.5855417 -3.715598 2.544515 0.8569269
## D-F-L-NF 0.8996250 -2.230431 4.029681 0.7011505
## D-F-L-F 1.4851667 -1.644890 4.615223 0.4064527
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.1806 0.18060 0.1457 0.7126
## Treat.Int 2 1.8191 0.90953 0.7340 0.5097
## Residuals 8 9.9129 1.23912
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -0.2453535 -1.727379 1.236672 0.7125759
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.5112879 -2.760441 1.737865 0.7978109
## D-F-L-NF 0.4415530 -1.807600 2.690706 0.8439105
## D-F-L-F 0.9528409 -1.296312 3.201994 0.4800982
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 2.301 2.3006 0.5090 0.4958
## Treat.Int 2 0.536 0.2681 0.0593 0.9428
## Residuals 8 36.157 4.5196
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -0.8757071 -3.706106 1.954692 0.4958429
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.1529924 -4.448466 4.142481 0.9943098
## D-F-L-NF 0.3519318 -3.943541 4.647405 0.9703487
## D-F-L-F 0.5049242 -3.790549 4.800398 0.9401608
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 10.0959 10.0959 5.0226 0.05533 .
## Treat.Int 2 1.9626 0.9813 0.4882 0.63089
## Residuals 8 16.0806 2.0101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -1.834472 -3.722052 0.05310792 0.0553284
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.9262500 -3.790881 1.938381 0.6413964
## D-F-L-NF -0.1589583 -3.023590 2.705673 0.9862591
## D-F-L-F 0.7672917 -2.097340 3.631923 0.7333288
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.9366 1.93656 0.8704 0.3781
## Treat.Int 2 1.1095 0.55476 0.2494 0.7851
## Residuals 8 17.7984 2.22480
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host 0.8034428 -1.182399 2.789285 0.3781308
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.3559343 -3.369690 2.657821 0.9396161
## D-F-L-NF -0.7445833 -3.758339 2.269172 0.7668729
## D-F-L-F -0.3886490 -3.402404 2.625106 0.9285371
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 15.4184 15.4184 15.5000 0.004314 **
## Treat.Int 2 7.1774 3.5887 3.6077 0.076423 .
## Residuals 8 7.9579 0.9947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -2.267039 -3.594905 -0.9391742 0.0043141
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.3928728 -2.4080690 1.622323 0.8458544
## D-F-L-NF 1.4084868 -0.6067093 3.423683 0.1749601
## D-F-L-F 1.8013597 -0.2138365 3.816556 0.0781114
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.4717 0.47171 0.3402 0.5758
## Treat.Int 2 1.7167 0.85836 0.6190 0.5624
## Residuals 8 11.0933 1.38666
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -0.3965321 -1.964311 1.171246 0.5757947
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF 0.805110680 -1.574183 3.184404 0.6164040
## D-F-L-NF 0.799570313 -1.579723 3.178864 0.6202649
## D-F-L-F -0.005540367 -2.384834 2.373753 0.9999756
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.4274 0.42736 0.2195 0.6519
## Treat.Int 2 3.7940 1.89702 0.9743 0.4181
## Residuals 8 15.5772 1.94716
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host 0.3774306 -1.480373 2.235234 0.6519364
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF 1.1068359 -1.712606 3.926277 0.5282887
## D-F-L-NF 1.2633138 -1.556128 4.082755 0.4438334
## D-F-L-F 0.1564779 -2.662964 2.975919 0.9862544
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.7150 0.71500 0.4577 0.5178
## Treat.Int 2 0.0828 0.04142 0.0265 0.9739
## Residuals 8 12.4980 1.56225
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -0.4881944 -2.152277 1.175888 0.5177911
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF 0.14959239 -2.375854 2.675039 0.9843613
## D-F-L-NF -0.04470109 -2.570148 2.480746 0.9985909
## D-F-L-F -0.19429348 -2.719740 2.331153 0.9737967
# Almost Fraction effect for Alanine, Proline
# Fraction effect for: Glycine, Glutamic acid
# Treatment effect for: Glycine
# Almost Treatment effect: Glutamic Acid
############ just host
for(i in c(11:23)){
Y=d13C.host[,i]
mod<-aov(Y~Treat.Int, data=d13C.host)
print(anova(mod))
#print(TukeyHSD(mod))
plot(allEffects(mod), ylab=colnames(d13C.host)[i], cex.axis=0.5, cex.lab=0.5)
}## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.0932 0.04660 0.0567 0.9459
## Residuals 3 2.4654 0.82179
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 10.8858 5.4429 7.9309 0.06343 .
## Residuals 3 2.0589 0.6863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 6.8484 3.4242 0.4379 0.681
## Residuals 3 23.4580 7.8193
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 6.8177 3.4088 1.3167 0.3886
## Residuals 3 7.7669 2.5890
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.9241 0.46205 0.1674 0.8533
## Residuals 3 8.2826 2.76086
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 1.5210 0.76052 0.5506 0.6256
## Residuals 3 4.1441 1.38137
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.2875 0.1438 0.0418 0.9596
## Residuals 3 10.3123 3.4374
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 1.21445 0.60723 2.347 0.2435
## Residuals 3 0.77618 0.25873
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.1362 1.06810 1.37 0.3778
## Residuals 3 2.3388 0.77962
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 4.3768 2.1884 1.5503 0.3448
## Residuals 3 4.2349 1.4116
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.9503 1.4751 0.9893 0.4677
## Residuals 3 4.4731 1.4910
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 5.4088 2.7044 1.4422 0.364
## Residuals 3 5.6255 1.8752
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.3085 1.15427 3.3645 0.1712
## Residuals 3 1.0292 0.34307
# If running only "Treat.Int" no effects accept p=0.06 for glycine
# If running "Light.Trt+Feed.Trt" then p=0.03 for glycine
############ just symbiont
for(i in c(11:23)){
Y=d13C.symb[,i]
mod<-aov(Y~Treat.Int, data=d13C.symb)
print(anova(mod))
#print(TukeyHSD(mod))
plot(allEffects(mod), ylab=colnames(d13C.symb)[i], cex.axis=0.5, cex.lab=0.5)
}## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 3.7832 1.8916 0.6219 0.5943
## Residuals 3 9.1245 3.0415
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 30.814 15.4072 2.7958 0.2063
## Residuals 3 16.533 5.5109
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.3166 1.1583 0.2831 0.7716
## Residuals 3 12.2743 4.0914
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 17.036 8.5178 8.3344 0.05957 .
## Residuals 3 3.066 1.0220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 4.4949 2.2475 0.676 0.5723
## Residuals 3 9.9741 3.3247
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.4550 0.2275 0.1216 0.8896
## Residuals 3 5.6118 1.8706
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.8795 0.4397 0.0523 0.9499
## Residuals 3 25.2135 8.4045
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 1.2539 0.6270 0.1271 0.8851
## Residuals 3 14.7986 4.9329
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 7.4714 3.7357 1.6099 0.335
## Residuals 3 6.9615 2.3205
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.9467 1.4733 1.2357 0.406
## Residuals 3 3.5769 1.1923
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.0713 0.03566 0.0201 0.9802
## Residuals 3 5.3153 1.77176
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 4.3532 2.1766 1.6391 0.3303
## Residuals 3 3.9837 1.3279
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 1.8423 0.92114 0.3734 0.7165
## Residuals 3 7.4008 2.46694
# If running only "Treat.Int" no effects
# If running "Light.Trt+Feed.Trt" then p=0.03 for serineFigures
-New dataframe (long format) here to make figures. Same data as above.
###########
###########
# CSAA.dat long
d13C.dat.long<-read.csv("data/d13C.CSIA.long.csv")
#str(d13C.dat.long)
d13C.dat.long<-d13C.dat.long[!(d13C.dat.long$Amino.acid=="Methionine"),]
d13C.dat.long<-d13C.dat.long[!(d13C.dat.long$Amino.acid=="Norleucine"),]
d13C.dat.long<-d13C.dat.long[!(d13C.dat.long$Amino.acid=="Aminoadipic Acid"),] # remove unwanted data
d13C.dat.long$AA.short<-mapvalues(d13C.dat.long$Amino.acid, from =c("Alanine", "Aspartic acid", "Glutamic acid", "Glycine", "Isoleucine", "Leucine", "Lysine", "Phenylalanine", "Proline", "Serine", "Threonine", "Tyrosine", "Valine"), to = c("Ala", "Asp", "Glx", "Gly", "Ile", "Leu", "Lys", "Phe", "Pro", "Ser", "Thr", "Tyr", "Val"))
d13C.dat.long$AA.short<-factor(d13C.dat.long$AA.short, levels=c("Ala","Asp", "Gly", "Glx", "Pro", "Ser", "Tyr", "Ile", "Leu", "Lys", "Phe", "Thr", "Val"))
d13C.dat.long$Treat.Int<-factor(d13C.dat.long$Treat.Int, levels=c("L-NF", "L-F", "D-F", "Plank"))
# looking at average essential and non-essential AA
d13C.dat.long$AA.cat<-ifelse(d13C.dat.long$AA.short=="Ala" |d13C.dat.long$AA.short=="Asp" |
d13C.dat.long$AA.short=="Gly" | d13C.dat.long$AA.short=="Glx" |
d13C.dat.long$AA.short=="Pro" | d13C.dat.long$AA.short=="Ser" |
d13C.dat.long$AA.short=="Tyr", "Non-EAA", "EAA")
dfC<-d13C.dat.longTable: Carbon Essential and Non-essential AA
##################
# all essentail and non-essential AA
AA.means<-aggregate(d13C.value~AA.cat+Treat.Int+Fraction, data=dfC, mean, na.rm=TRUE); AA.means
AA.sd<-aggregate(d13C.value~AA.cat+Treat.Int+Fraction, data=dfC, sd, na.rm=TRUE)
colnames(AA.sd)[4]="SD"
AA.means<-cbind(AA.means, AA.sd[4])
AA.means$Fraction<-factor(AA.means$Fraction, levels=c("host", "symb", "plank"))- d13C by fraction and treatments, showing d13C.CSIA_frac.trt
#################
#################
# d13C by fraction and treatments
df.mean<-aggregate(d13C.value~AA.short+Fraction+Treat.Int, data=dfC, mean, na.rm=TRUE)
df.n<-aggregate(d13C.value~AA.short+Fraction+Treat.Int, data=dfC, length)
df.SD<-aggregate(d13C.value~AA.short+Fraction+Treat.Int, data=dfC, sd, na.rm=TRUE)
colnames(df.SD)[4]="SD"
df.mean<-cbind(df.mean, df.SD[4])
# replace NA for plankton SD as 0
df.mean$SD[is.na(df.mean$SD)] <- 0
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
df.mean$Treat.Int<-factor(df.mean$Treat.Int, levels=c("L-NF", "L-F", "D-F", "Plank"))
## d13C just by fraction
df.mean.frac<-aggregate(d13C.value~AA.short+Fraction, data=dfC, mean, na.rm=TRUE)
df.n.frac<-aggregate(d13C.value~AA.short+Fraction, data=dfC, length)
df.SE.frac<-aggregate(d13C.value~AA.short+Fraction, data=dfC, std.error, na.rm=TRUE)
colnames(df.SE.frac)[3]="SE"
df.SE.frac[is.na(df.SE.frac)] <- 0
df.mean.frac<-cbind(df.mean.frac, df.SE.frac[3])
df.mean.frac$Fraction<-factor(df.mean.frac$Fraction, levels=c("host", "symb", "plank"))
write.csv(df.mean.frac, "output/d13C.mean.frac.csv")
######## Figures
Fig.formatting<-(theme_classic()) +
theme(text=element_text(size=10),
axis.line=element_blank(),
legend.text.align = 0,
legend.text=element_text(size=10),
#legend.title = element_blank(),
panel.border = element_rect(fill=NA, colour = "black", size=1),
aspect.ratio=1,
axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=10),
axis.text.x=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=8)) +
theme(legend.key.size = unit(0.4, "cm")) +
theme(aspect.ratio=1) +
theme(panel.spacing=unit(c(0, 0, 0, 0), "cm"))
#######
pd <- position_dodge(0.5) #offset for error bars
d13C.CSIA_frac.trt<-ggplot(df.mean, aes(x=AA.short, y=d13C.value)) +
geom_point(size=2, position=pd, aes(shape=Treat.Int, color=Fraction, group=Treat.Int)) +
geom_errorbar(aes(ymin=d13C.value-SD, ymax=d13C.value+SD, color=Fraction, group=Treat.Int),
size=.5, width=0, position=pd) +
ggtitle(expression(paste(delta^{13}, C[AA], " by biological fraction and treatment"))) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Nonessential-AA", x=4, y=0, color="gray40") +
annotate(geom="text", label="Essential-AA", x=10, y=0, color="gray40") +
coord_cartesian(ylim=c(-35, 0)) +
xlab("Amino Acids") +
scale_color_manual(values=c(frac.color)) +
ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)"))) +
Fig.formatting
#print(d13C.CSIA_frac.trt)
#ggsave("figures/carbon/d13C.CSIA_frac.trt.pdf", height=5, width=8, encod="MacRoman")- all d13C amino acids: showing d13C.CSIA_Trt.alone – move to Supplement
##################
# all d13C amino acids
df.mean2<-aggregate(d13C.value~AA.short+Treat.Int, data=dfC, mean, na.rm=TRUE)
df.SE2<-aggregate(d13C.value~AA.short+Treat.Int, data=dfC, na.rm=TRUE, std.error)
df.n2<-aggregate(d13C.value~AA.short+Treat.Int, data=dfC, length)
df.SE2[is.na(df.SE2)] <- 0
colnames(df.SE2)[3]="SE"
df.mean2<-cbind(df.mean2, df.SE2[3])
pd <- position_dodge(0.5) #offset for error bars
d13C.CSIA_Trt.alone<-ggplot(df.mean2, aes(x=AA.short, y=d13C.value, group=Treat.Int)) +
geom_errorbar(aes(ymin=d13C.value-SE, ymax=d13C.value+SE, color=Treat.Int), size=.5, width=0, position=pd) +
geom_point(aes(color=Treat.Int, shape=Treat.Int), size=2, position=pd) +
scale_shape_manual(values=c(19,19,19,3))+
scale_color_manual(values=trt.color)+
ggtitle(expression(paste(delta^{13}, C[AA], " by treatment"))) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Nonessential-AA", x=4, y=0, color="gray40") +
annotate(geom="text", label="Essential-AA", x=10, y=0, color="gray40") +
coord_cartesian(ylim=c(-35, 0)) +
xlab("Amino Acids") +
ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)"))) +
Fig.formatting
#print(d13C.CSIA_Trt.alone)
#ggsave("figures/carbon/d13C.CSIA_Trt.alone.pdf", height=5, width=8, encod="MacRoman")- d13C by fraction, showing d13C.CSIA_frac – move to Supplement
# all AA pooled by fraction
df.mean3<-aggregate(d13C.value~AA.short+Fraction, data=dfC, mean, na.rm=TRUE)
df.SE3<-aggregate(d13C.value~AA.short+Fraction, data=dfC, na.rm=TRUE, std.error)
df.n3<-aggregate(d13C.value~AA.short+Fraction, data=dfC, length)
df.SE3[is.na(df.SE3)] <- 0
colnames(df.SE3)[3]="SE"
df.mean3<-cbind(df.mean3, df.SE3[3])
df.mean3$Fraction<-factor(df.mean3$Fraction, levels=c("host", "symb", "plank"))
pd <- position_dodge(0.5) #offset for error bars
d13C.CSIA_Fraction<- ggplot(df.mean3, aes(x=AA.short, y=d13C.value)) +
geom_errorbar(aes(ymin=d13C.value-SE, ymax=d13C.value+SE, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(size=2, position=pd, aes(color=Fraction, shape=Fraction)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Nonessential-AA", x=4, y=0, color="gray40") +
annotate(geom="text", label="Essential-AA", x=10, y=0, color="gray40") +
ggtitle(expression(paste(delta^{13}, C[AA], " by biological fraction"))) +
coord_cartesian(ylim=c(-35, 0)) +
xlab("Amino Acids") +
ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)"))) +
scale_color_manual(values=frac.color) +
scale_shape_manual(values=c(19,19,3))+
Fig.formatting
#print(d13C.CSIA_Fraction)
#ggsave("figures/carbon/d13C.CSIA_Fraction.pdf", height=5, width=8, encod="MacRoman")Nitrogen AA
Models
Overall we see:
- Fraction effect for: Leucine, Proline, Aspartic Acid, Glutamic Acid, Tyrosine.
- Treatment effect for: Leucine
d15N.dat2<-d15N.dat[!(d15N.dat$Fraction=="plank"),] #remove plankton for now
for(i in c(11:23)){
Y=d15N.dat2[,i]
mod<-aov(Y~Fraction+Treat.Int, data=d15N.dat2)
print(anova(mod), cex=0.5)
#print(TukeyHSD(mod))
plot(allEffects(mod), ylab=colnames(d15N.dat2)[i], cex.axis=0.5)
}## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 8.1676 8.1676 3.3922 0.1028
## Treat.Int 2 1.8579 0.9290 0.3858 0.6919
## Residuals 8 19.2618 2.4077
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 4.2262 4.2262 1.6503 0.2349
## Treat.Int 2 1.5693 0.7846 0.3064 0.7444
## Residuals 8 20.4868 2.5609
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 13.327 13.3269 2.6576 0.1417
## Treat.Int 2 3.612 1.8061 0.3602 0.7083
## Residuals 8 40.117 5.0147
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.0006 0.00062 0.0003 0.9860
## Treat.Int 2 1.6612 0.83058 0.4397 0.6589
## Residuals 8 15.1127 1.88908
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.2248 0.22482 0.2201 0.6515
## Treat.Int 2 0.0415 0.02076 0.0203 0.9799
## Residuals 8 8.1699 1.02123
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 9.9952 9.9952 19.9043 0.002107 **
## Treat.Int 2 3.8088 1.9044 3.7924 0.069430 .
## Residuals 8 4.0173 0.5022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.3383 0.3383 0.0908 0.7708
## Treat.Int 2 3.8576 1.9288 0.5178 0.6145
## Residuals 8 29.8002 3.7250
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 22.9354 22.9354 28.9356 0.0006623 ***
## Treat.Int 2 4.1174 2.0587 2.5973 0.1351371
## Residuals 8 6.3411 0.7926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 3.3328 3.3328 6.3165 0.03619 *
## Treat.Int 2 1.4198 0.7099 1.3455 0.31354
## Residuals 8 4.2210 0.5276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.1145 1.11452 2.3608 0.1630
## Treat.Int 2 0.9348 0.46741 0.9901 0.4129
## Residuals 8 3.7768 0.47210
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.4002 1.40019 0.5355 0.4852
## Treat.Int 2 0.8007 0.40036 0.1531 0.8605
## Residuals 8 20.9164 2.61455
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 16.163 16.163 8.9499 0.01729 *
## Treat.Int 2 0.128 0.064 0.0354 0.96532
## Residuals 8 14.448 1.806
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.1051 0.10513 0.0799 0.7846
## Treat.Int 2 0.2452 0.12259 0.0931 0.9120
## Residuals 8 10.5298 1.31623
# Fraction effect for: Leucine, Proline, Aspartic Acid, Glutamic Acid, Tyrosine
# Treatment effect for: LeucineFigures
###########
###########
# d15N.CSIA.dat long
d15N.dat.long<-read.csv("data/d15N.CSIA.long.csv")
#str(d15N.dat.long)
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Methionine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Norleucine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Aminoadipic Acid"),] # remove unwanted data
d15N.dat.long$AA.short<-mapvalues(d15N.dat.long$Amino.acid, from =c("Alanine", "Aspartic acid", "Glutamic acid", "Glycine", "Isoleucine", "Leucine", "Lysine", "Phenylalanine", "Proline", "Serine", "Threonine", "Tyrosine", "Valine"), to = c("Ala", "Asp", "Glx", "Gly", "Ile", "Leu", "Lys", "Phe", "Pro", "Ser", "Thr", "Tyr", "Val"))
d15N.dat.long$AA.short<-factor(d15N.dat.long$AA.short, levels=c("Ala","Asp", "Glx", "Ile", "Leu", "Pro", "Val", "Gly", "Lys", "Ser", "Phe", "Thr", "Tyr"))
# looking at average Trophic and Source
d15N.dat.long$AA.cat<-ifelse(d15N.dat.long$AA.short=="Asp" | d15N.dat.long$AA.short=="Glx" |
d15N.dat.long$AA.short=="Ala" | d15N.dat.long$AA.short=="Ile" |
d15N.dat.long$AA.short=="Leu" | d15N.dat.long$AA.short=="Val" |
d15N.dat.long$AA.short=="Pro", "Troph", "Source")
d15N.dat.long$Treat.Int<-factor(d15N.dat.long$Treat.Int, levels=c("L-NF", "L-F", "D-F", "Plank"))
dfN<-d15N.dat.long- all AA pooled by fraction, showing d15N.CSIA_Fraction
######## Figures
pd <- position_dodge(0.5) #offset for error bars
df.mean<-aggregate(d15N.value~AA.short+Fraction, data=dfN, mean, na.rm=TRUE)
df.n<-aggregate(d15N.value~AA.short+Fraction, data=dfN, length)
df.SE<-aggregate(d15N.value~AA.short+Fraction, data=dfN, std.error, na.rm=TRUE)
df.SE[is.na(df.SE)] <- 0
colnames(df.SE)[3]="SE"
df.mean<-cbind(df.mean, df.SE[3])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
write.csv(df.mean, "output/d15N.frac.meanse.csv")
Fig.formatting<-(theme_classic()) +
theme(text=element_text(size=10),
axis.line=element_blank(),
legend.text.align = 0,
legend.text=element_text(size=10),
#legend.title = element_blank(),
panel.border = element_rect(fill=NA, colour = "black", size=1),
aspect.ratio=1,
axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=10),
axis.text.x=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=8)) +
theme(legend.key.size = unit(0.4, "cm")) +
theme(aspect.ratio=1) +
theme(panel.spacing=unit(c(0, 0, 0, 0), "cm"))
######
# all AA pooled by fraction
d15N.CSIA_Fraction<-ggplot(df.mean, aes(x=AA.short, y=d15N.value)) +
geom_errorbar(aes(ymin=d15N.value-SE, ymax=d15N.value+SE, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(size=2, position=pd, aes(color=Fraction, shape=Fraction)) +
scale_shape_manual(values=c(19,19,3))+
ggtitle(expression(paste(delta^{15}, N[AA], " by biological fraction"))) +
coord_cartesian(ylim=c(-5, 15)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=15, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=15, color="gray40") +
xlab("Amino Acids") +
ylab(expression(paste(delta^{15}, N[AA], " (\u2030, Air)"))) +
scale_color_manual(values=frac.color)+
Fig.formatting
#print(d15N.CSIA_Fraction)
#ggsave("figures/nitrogen/d15N.CSIA_Fraction.pdf", height=5, width=8, encod="MacRoman")- Fraction and treatment, showing d15N.CSIA_frac.trt
#################
df.mean<-aggregate(d15N.value~AA.short+Fraction+Treat.Int, data=dfN, mean, na.rm=TRUE)
df.n<-aggregate(d15N.value~AA.short+Fraction+Treat.Int, data=dfN, length)
df.SD<-aggregate(d15N.value~AA.short+Fraction+Treat.Int, data=dfN, sd, na.rm=TRUE)
colnames(df.SD)[4]="SD"
df.SD[is.na(df.SD)] <- 0
df.mean<-cbind(df.mean, df.SD[4])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
df.mean$Treat.Int<-factor(df.mean$Treat.Int, levels=c("L-NF", "L-F", "D-F", "Plank"))
d15N.CSIA_frac.trt<-ggplot(df.mean, aes(x=AA.short, y=d15N.value)) +
geom_point(size=2, aes(shape=Treat.Int, color=Fraction, group=Treat.Int), position=pd) +
geom_errorbar(aes(ymin=d15N.value-SD, ymax=d15N.value+SD, group=Treat.Int, color=Fraction),
size=.5, width=0, position=pd) +
ggtitle(expression(paste(delta^{15}, N[AA], " by biological fraction, treatment"))) +
coord_cartesian(ylim=c(-5, 15)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=15, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=15, color="gray40") +
xlab("Amino Acids") +
scale_color_manual(values=frac.color) +
ylab(expression(paste(delta^{15}, N[AA], " (\u2030, Air)"))) +
Fig.formatting
#print(d15N.CSIA_frac.trt)
#ggsave("figures/nitrogen/d15N.CSIA_frac.trt.pdf", height=5, width=8, encod="MacRoman")- all amino acids, showing d15N.CSIA_Trt.alone.pdf
##################
# all d15N amino acids
df.mean2<-aggregate(d15N.value~AA.short+Treat.Int, data=dfN, mean, na.rm=TRUE)
df.n2<-aggregate(d15N.value~AA.short+Treat.Int, data=dfN, length)
df.SE2<-aggregate(d15N.value~AA.short+Treat.Int, data=dfN, na.rm=TRUE, std.error)
df.SE2[is.na(df.SE2)] <- 0
colnames(df.SE2)[3]="SE"
df.mean2<-cbind(df.mean2, df.SE2[3])
d15N.CSIA_Trt.alone<-ggplot(df.mean2, aes(x=AA.short, y=d15N.value, group=Treat.Int)) +
geom_errorbar(aes(ymin=d15N.value-SE, ymax=d15N.value+SE, color=Treat.Int), size=.5, width=0, position=pd) +
geom_point(aes(color=Treat.Int, shape=Treat.Int), size=2, position=pd) +
ggtitle(expression(paste(delta^{15}, N[AA], " by treatment"))) +
coord_cartesian(ylim=c(-5, 15)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=15, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=15, color="gray40") +
xlab("Amino Acids") +
scale_color_manual(values=trt.color) +
scale_shape_manual(values=c(19,19,19,3))+
ylab(expression(paste(delta^{15}, N[AA], " (\u2030, Air)"))) +
Fig.formatting
#print(d15N.CSIA_Trt.alone)
#ggsave("figures/nitrogen/d15N.CSIA_Trt.alone.pdf", height=5, width=8, encod="MacRoman")Figure 3– AA.frac-by-treatment combined
leg1 <- get_legend(
# create some space to the left of the legend
d13C.CSIA_frac.trt + theme(legend.box.margin = margin(0, 0, 0, 12)))
AA.frac.trt<- plot_grid(d13C.CSIA_frac.trt + ggtitle("")+
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(a)", x=1, y=0, color="black"),
d15N.CSIA_frac.trt + ggtitle("") +
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(b)", x=1, y=15, color="black"),
ncol=2,nrow=1)
plot_grid(AA.frac.trt, leg1, rel_widths = c(8, 1)) # legend 1/8 size as first obj.
Figure 3. (a) Carbon and (b) nitrogen isotope analysis of individual amino acids in coral (host), symbiont algae (symb), and a pooled plankton sample (plank) and in host and symbiont tissues in response to nutrition treatment: Dark-Fed (D-F), Light-Fed (L-F), and Light-Not Fed (L-NF). Values are mean ± SD (n=2), except for the plankton sample (n=1).
dev.copy(pdf, "figures/Fig 3.AA.frac.trt.pdf", width = 10, height = 5, encod="MacRoman")
dev.off()Supplemental Figure S1c,d– AA.frac combined
leg3 <- get_legend(
# create some space to the left of the legend
d13C.CSIA_Fraction + theme(legend.box.margin = margin(0, 0, 0, 12)))
AA.trt<- plot_grid(d13C.CSIA_Fraction + ggtitle("")+
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(a)", x=1, y=0, color="black"),
d15N.CSIA_Fraction + ggtitle("") +
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(b)", x=1, y=15, color="black"),
ncol=2,nrow=1)
plot_grid(AA.trt, leg3, rel_widths = c(8, 1)) # legend 1/8 size as first obj.
Figure S1. (a) Carbon and (b) nitrogen isotope analysis of individual amino acids in coral (host), symbiont algae (symb), and a pooled plankton sample (plank). Values are mean ± SE (n=6), except for the plankton sample (n=1).
dev.copy(pdf, "figures/Fig S2.AA.frac.pdf", width = 10, height = 5, encod="MacRoman")
dev.off()Supplemental Figure S1a,b– AA.trt plot
leg2 <- get_legend(
# create some space to the left of the legend
d13C.CSIA_Trt.alone + theme(legend.box.margin = margin(0, 0, 0, 12)))
AA.trt<- plot_grid(d13C.CSIA_Trt.alone + ggtitle("")+
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(a)", x=1, y=0, color="black"),
d15N.CSIA_Trt.alone + ggtitle("") +
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(b)", x=1, y=15, color="black"),
ncol=2,nrow=1)
plot_grid(AA.trt, leg2, rel_widths = c(8, 1)) # legend 1/8 size as first obj.
Figure S1. (c) Carbon and (d) nitrogen isotope analysis of individual amino acids in coral (host), symbiont algae (symb), and a pooled plankton sample (plank). Values are mean ± SD (n=2), except for the plankton sample (n=1).
dev.copy(pdf, "figures/Fig S1.AA.trt.pdf", width = 10, height = 5, encod="MacRoman")
dev.off()Trophic position and proxy
Trophic position using trophic AA gultamic acid (Glx) and source amino acid phenylalanine (Phe), following Chikaraishi et al. 2009
\(Trophic~position~(TP){_C}{_S}{_I}{_A}= [(δ{^1}{^5}N{_T}{_r}{_p}~-~δ{^1}{^5}N{_S}{_r}{_c})-B/~TDF{_A}{_A} +1\)
- Trophic Position, showing TP.glx.phe
###
# scatter of glutamic.acid vs. phenylalanine
### ### ###
### ### ### trophic position using trophic (Glx) and source (Phe) AA, Chikaraishi et al. 2009
d15N.dat # dataframe here
# glx = trophic AA (changing with food, show enrichment realtive to source
# phe = source AA (showlittle change with increasing trophic position, reflect d15N baseline)
# beta = 3.4 (difference in d15N values among trophic and source AAs in primary producers, @ TP=1)
# TDFAA = trophic discrimination factor: mean 15N enrichment of >=1 trophic vs. source AA per trophic level
d15N.dat<-d15N.dat %>% mutate(TP = ((Glutamic.acid - Phenylalanine - 3.4)/7.6) +1)
# run a model
TP.df<-d15N.dat
TP.df<-TP.df[!(TP.df$Fraction=="plank"),] # remove plankton
anova(lm(TP~Fraction+Treat.Int, data=TP.df)) # no difference
df.mean<-aggregate(TP~Treat.Int+Fraction, data=d15N.dat, mean, na.rm=TRUE)
df.n<-aggregate(TP~Treat.Int+Fraction, data=d15N.dat, length)
df.SD<-aggregate(TP~Treat.Int+Fraction, data=d15N.dat, sd, na.rm=TRUE)
colnames(df.SD)[3]="SD"
df.mean<-cbind(df.mean, df.SD[3])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
df.mean$Treat.Int<-factor(df.mean$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
write.csv(d15N.dat, "output/TP.raw.csv")
TP<-ggplot(df.mean, aes(x=Treat.Int, y=TP)) +
geom_errorbar(aes(ymin=TP-SD, ymax=TP+SD, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(size=2, position=pd, aes(color=Fraction, shape=Fraction)) +
ggtitle("Chikaraishi trophic position") +
coord_cartesian(ylim=c(0, 3)) +
scale_x_discrete(name ="Treatments",
labels=c("Light\nNot Fed", "Light\nFed", "Dark\nFed", "Plankton")) +
ylab(expression(paste("TP "~delta^{15}, N[Glx-Phe], " (\u2030, Air)"))) +
scale_shape_manual(values=c(19,19,3))+
scale_color_manual(values=frac.color) +
Fig.formatting
#print(TP)
#ggsave("figures/nitrogen/TP.glx.phe.pdf", height=5, width=8, encod="MacRoman")Sum V
Calculate Sum-V, McCarthy et al. 2007. The sum-V parameter is a proxy for total heterotrophic resynthesis. It is defined as the average deviation in the d15N values of the trophic amino acids Ala, Asp, Glx, Ile, Leu, and Pro.
- showing d15N.sumV.CSIA move to supplement
###########
###########
# d15N.CSIA.dat long
d15N.dat.long<-read.csv("data/d15N.CSIA.long.csv")
#str(d15N.dat.long)
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Methionine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Norleucine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Aminoadipic Acid"),] # remove unwanted data
d15N.dat.long$AA.short<-mapvalues(d15N.dat.long$Amino.acid, from =c("Alanine", "Aspartic acid", "Glutamic acid", "Glycine", "Isoleucine", "Leucine", "Lysine", "Phenylalanine", "Proline", "Serine", "Threonine", "Tyrosine", "Valine"), to = c("Ala", "Asp", "Glx", "Gly", "Ile", "Leu", "Lys", "Phe", "Pro", "Ser", "Thr", "Tyr", "Val"))
d15N.dat.long$AA.short<-factor(d15N.dat.long$AA.short, levels=c("Ala","Asp", "Glx", "Ile", "Leu", "Pro", "Val", "Gly", "Lys", "Ser", "Phe", "Thr", "Tyr"))
# looking at average Trophic and Source
d15N.dat.long$AA.cat<-ifelse(d15N.dat.long$AA.short=="Asp" | d15N.dat.long$AA.short=="Glx" |
d15N.dat.long$AA.short=="Ala" | d15N.dat.long$AA.short=="Ile" |
d15N.dat.long$AA.short=="Leu" | d15N.dat.long$AA.short=="Val" |
d15N.dat.long$AA.short=="Pro", "Troph", "Source")
d15N.dat.long$Treat.Int<-factor(d15N.dat.long$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
dfN<-d15N.dat.long
###########################
###########################
###########################
# dfN is dataframe
sV.df<-dfN[!(dfN$AA.short=="Thr"), ] # not good source, remove here
# make dataframe for AA, that with su deviance = sum-V
sV.df<-sV.df[c(sV.df$AA.short=="Ala" | sV.df$AA.short=="Glx" | sV.df$AA.short=="Asp" |
sV.df$AA.short=="Ile" | sV.df$AA.short=="Leu" | sV.df$AA.short=="Pro"),]
#write.csv(sV.df, "sumV.csv")
sumVdf<-read.csv("data/sumV.csv")
sumVdf$Fraction<-factor(sumVdf$Fraction, levels=c("host", "symb", "plank"))
sumVdf$Treat.Int<-factor(sumVdf$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
######## model
sumVdf.mod<-sumVdf[!(sumVdf$Fraction=="plank"),] # remove plankton
anova(lm(sumV~Fraction + Treat.Int, data=sumVdf.mod)) # no difference
########
sumVdf.mean<-aggregate(sumV~AA.short+AA.cat+Fraction+Treat.Int, data=sumVdf, mean, na.rm=TRUE)
sumVdf.n<-aggregate(sumV~AA.short+AA.cat+Fraction+Treat.Int, data=sumVdf, length)
sumVdf.sd<-aggregate(sumV~AA.short+AA.cat+Fraction+Treat.Int, data=sumVdf, sd, na.rm=TRUE)
sumVdf<-cbind(sumVdf.mean, sumVdf.sd[5])
colnames(sumVdf)[5]<-"mean.sumV"
colnames(sumVdf)[6]="SD"
sumVdf[is.na(sumVdf)] <- 0
sumVdf$Treat.Int<- factor(sumVdf$Treat.Int, levels=c("L-NF", "L-F", "D-F"))
pd <- position_dodge(0.7) #offset for error bars and columns
sumV<-ggplot(sumVdf, aes(x=Treat.Int, y=mean.sumV)) +
geom_point(aes(color=Fraction), size=2, position=pd) +
geom_errorbar(aes(ymin=mean.sumV-SD, ymax=mean.sumV+SD, color=Fraction),
size=.5, width=0, position=pd) +
coord_cartesian(ylim=c(0, 3))+
ggtitle(expression(paste("Sum-V ", delta^{15}, N[AA]))) +
scale_color_manual(values=frac.color) +
scale_x_discrete(name ="Treatments",
labels=c("Light\nNot Fed", "Light\nFed", "Dark\nFed", "Plankton")) +
ylab(expression(paste(delta^{15}, N[Sum-V], " (\u2030, Air)"))) +
Fig.formatting
#print(sumV)
#ggsave("figures/nitrogen/d15N.sumV.CSIA.pdf", height=5, width=8, encod="MacRoman")##### Weighted means These are the weighted means for trophic and source AA following Bradley et al. 2015. Weighted mean AA δ15N values.
- weighted means, showing wt.mean.d15N.CSIA supplement
###########################
##################
#########
df.mean<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=dfN, mean, na.rm=TRUE)
df.n<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=dfN, length)
df.SD<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=dfN, sd, na.rm=TRUE)
colnames(df.SD)[5]="SD"
df.mean<-cbind(df.mean, df.SD[5])
df.mean$mean.sd<-(df.mean$d15N.value/df.mean$SD)
df.mean$inv.sd<-(1/df.mean$SD)
# write.csv(df.mean, "wtmeans.csv") # use this to calculate weighted mean
# weighted mean is sum(mean.sd/inv.sd) for trophic AA, same for source AA
# delta.Tr.So (below) is difference in (weighted mean) Trophic AA - Source AA for host or symb, per treatment
#########
wt.mean<-read.csv("data/wt.means.d15N.csv")
wt.mean.df<-aggregate(wt.mean~AA.cat+Fraction+Treat.Int, data=wt.mean, mean, na.rm=TRUE)
SD<-aggregate(wt.SD~AA.cat+Fraction+Treat.Int, data=wt.mean, mean, na.rm=TRUE); colnames(SD)[4]<-"wt.SD"
wt.mean.df<-cbind(wt.mean.df, SD[4])
wt.mean.df$Treat.Int<- factor(wt.mean.df$Treat.Int, levels=c("L-NF", "L-F", "D-F"))
######## model
wt.mean.mod<-wt.mean[!(wt.mean$Fraction=="plank"),]
anova(lm(wt.mean~Fraction + Treat.Int, data=wt.mean.mod)) # no difference
########
pd <- position_dodge(0.5) #offset for error bars
ggplot(wt.mean.df, aes(x=Treat.Int, y=wt.mean)) +
geom_point(aes(color=Fraction, shape=AA.cat), size=3, position=pd) +
geom_errorbar(aes(ymin=wt.mean-wt.SD, ymax=wt.mean+wt.SD, color=Fraction, shape=AA.cat),
size=.5, width=0, position=pd) +
coord_cartesian(ylim=c(0, 12))+
ggtitle(expression(paste("Weighted Mean ", delta^{15}, N[AA]))) +
scale_color_manual(values=c("coral", "springgreen3")) +
scale_x_discrete(name ="Treatments",
labels=c("Light\nNot Fed", "Light\nFed", "Dark\nFed")) +
ylab(expression(paste(delta^{15}, N[AA], " (\u2030, Air)"))) +
Fig.formatting
Figure S3. δ15NAA weighted means for trophic and source amino acids according to coral tissue fractions (host, symbiont) and experimental nutrition-treatments. Values are mean ± SD (n=2).
ggsave("figures/Fig S3.wt.mean.d15N.CSIA.pdf", height=5, width=8, encod="MacRoman")Supplemental Figure S2a,b– TP and Sum V combined
leg4 <- get_legend(
# create some space to the left of the legend
TP + theme(legend.box.margin = margin(0, 0, 0, 12)))
TP.sumV.plots<- plot_grid(TP + ggtitle("")+
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=2.2, label="(a)", x=0.7, y=3, color="black"),
sumV + ggtitle("") +
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=2.2, label="(b)", x=0.7, y=3, color="black"),
ncol=2,nrow=1)
plot_grid(TP.sumV.plots, leg4, rel_widths = c(6, 1)) # legend 1/3 size as first obj.
Figure S2. (a) Trophic position and (b) sum-V calculations for nitrogen isotope analysis of individual amino acids in coral (host) and symbiont algae (symb) in response to nutrition treatment and in relation to a pooled plankton sample (plank). Values are mean ± SD (n=2), except for the plankton sample (n=1).
dev.copy(pdf, "figures/Fig S3.TP.sumV.pdf", width = 7, height = 5, encod="MacRoman")
dev.off()LDA for normalized Pacific data
Mean-normalized data is important for comparing across species, times, or locations. This is accomplished by taking the across sample mean for the amino acid (AA) of choice, then subtracting the raw AA value from the mean. We did this for the essential AA (EAA) of carbon: Leucine, Isoleucine, Phenylalanine, Threonine, Lysine, Valine.
Uses linear discrimination analyses (LDA) of the mean-normalized EAA in symbiont and plankton+POM from Oahu (this study) and those reported in Fox et al (2019, FuncEcol) to act as training data for sources of autotrophic vs. heterotrophic nutrition. The training data is then applied to the coral species in the two studies (M. capitata and Pocillopora meandrina) to assign coral IDs, asking if the corals align with either autotrophic or heterotrophic groups.
Combine the data from Oahu and Palmyra ### d13C-AA data from Fox et al
##### Palmyra data
Fox.data<-read.csv("data/Palmyra_AAd13C.csv")
# rename a column
names(Fox.data)[names(Fox.data)=="group"] <- "Fraction"
# rename a level
Fox.data$Fraction<- revalue(Fox.data$Fraction, c("Animal"="host.Pal", "Zoox"="symb.Pal",
"Plankton"="plank.Pal", "POM"="POM.Pal"))
# select columns
Fox.data<- Fox.data %>%
select(Location, Species, Fraction, Ala13C, Gly13C, Thr13C, Ser13C, Val13C, Leu13C, Ile13C, Pro13C, Asp13C, Glx13C, Phe13C, Tyr13C, Lys13C)
# rename AA
Fox.data<- plyr::rename(Fox.data, c("Ala13C"="Ala", "Gly13C"="Gly", "Thr13C" = "Thr",
"Ser13C" = "Ser", "Val13C" = "Val", "Leu13C" = "Leu",
"Ile13C" = "Ile", "Pro13C"= "Pro", "Asp13C" = "Asp",
"Glx13C" = "Glx", "Phe13C" = "Phe", "Tyr13C" = "Tyr", "Lys13C" = "Lys"))
#### Kaneohe, Oahu data
Wall.data<-d13C.dat
# add a Location column
Wall.data$Location<- "Oahu"
# rename a level
Wall.data$Fraction<- revalue(Wall.data$Fraction, c("host"="host.HI", "symb"="symb.HI",
"plank"="plank.HI"))
# select columns
Wall.data<-Wall.data %>%
select(Location, Species, Fraction, Alanine, Glycine, Threonine, Serine, Valine, Leucine, Isoleucine, Proline, Aspartic.acid, Glutamic.acid, Phenylalanine, Tyrosine, Lysine)
# make names the same for both df
Wall.data<- plyr::rename(Wall.data, c("Alanine"="Ala", "Glycine"="Gly", "Threonine" = "Thr",
"Serine" = "Ser", "Valine" = "Val", "Leucine" = "Leu",
"Isoleucine" = "Ile", "Proline"= "Pro", "Aspartic.acid" = "Asp",
"Glutamic.acid" = "Glx", "Phenylalanine" = "Phe",
"Tyrosine" = "Tyr", "Lysine" = "Lys"))
# combine the dataframes
Pacific.AA<-rbind(Fox.data, Wall.data)
Pacific.AA$Fraction<-factor(Pacific.AA$Fraction, levels=c("host.Pal", "host.HI", "symb.Pal", "symb.HI",
"plank.Pal", "plank.HI", "POM.Pal"))Run LDA analysis as performed in Fox et al.
######## For manuscript
####### using normalized data
####### run LDA with 2 sources: (1) plankton+POM, (2) symbiont Palmyra
####### use the 2 sources as training data for Hawaii
####### display points according to location (Palmyra or Hawaii)
##########
###########
### Raw data = 'Pacific.AA'
# LDA palmyra POM + plank, with Hawaii dataset
# 6 EAA
Pacific.EAA.df<- Pacific.AA %>%
select(Location, Species, Fraction, Leu, Ile, Phe, Thr, Lys, Val)
write.csv(Pacific.EAA.df, "output/Pacific.EAA.df.csv")
###### ###### ###### ###### ######
###### NORMALIZING AAESS DATA BY Sample MEAN (mean of all AA d13C) - raw individual AAd13C value
# raw compiled data is 'Pacific.EAA.df'
Pac.ess.norm<-Pacific.EAA.df
#make ID column to run the normalization
Pac.ess.norm$ID<-1:nrow(Pac.ess.norm)
for(i in 1:length(Pac.ess.norm$ID)){
Pac.ess.norm$Ile.n[i] <- (Pac.ess.norm$Ile[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Leu.n[i] <- (Pac.ess.norm$Leu[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Lys.n[i] <- (Pac.ess.norm$Lys[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Phe.n[i] <- (Pac.ess.norm$Phe[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Thr.n[i] <- (Pac.ess.norm$Thr[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Val.n[i] <- (Pac.ess.norm$Val[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
}
Pac.ess.norm<-Pac.ess.norm[,c(1:3,10:16)] # drop raw columns and reorder
###############
############### Normalized LDA data is 'Pac.ess.norm.df'
# add new column of factors
Pac.ess.norm$Fraction.grouped<-Pac.ess.norm$Fraction
#rename these factors to animal, symbiont, plankton, POM
Pac.ess.norm$Fraction.grouped<-revalue(Pac.ess.norm$Fraction.grouped,
c("host.Pal"="animal", "host.HI"="animal",
"symb.Pal"="symbiont", "symb.HI"="symbiont",
"plank.Pal"="plankton", "plank.HI"="plankton",
"POM.Pal"="POM"))
#reorder
Pac.ess.norm.df<-Pac.ess.norm[,c(1:3,11,4:10)]
# separate out animal
# plankton, POM, symb = "zp"
Pac.EAA.norm.zp<-
Pac.ess.norm.df[!(Pac.ess.norm.df$Fraction.grouped=="animal"),]
# host animal = "ho"
Pac.EAA.norm.ho<-
Pac.ess.norm.df[(Pac.ess.norm.df$Fraction.grouped=="animal"),]
# drop factor levels
Pac.EAA.norm.zp<-droplevels(Pac.EAA.norm.zp)
Pac.EAA.norm.ho<-droplevels(Pac.EAA.norm.ho)
# remove ID columns for now
Pac.EAA.frac.norm.zp<-Pac.EAA.norm.zp[,c(-1:-3,-5)]
Pac.EAA.frac.norm.ho<-Pac.EAA.norm.ho[,c(-1:-3,-5)]
############
############ training for leave one out: how well can we estimate the sources?
# with CV = true, results for classes and posteriors are for cross validation (CV, leave one out)
LDA.Pac.EAA.frac.norm.zp <- lda(Fraction.grouped ~ Ile.n + Leu.n + Lys.n + Phe.n + Thr.n + Val.n, data = Pac.EAA.frac.norm.zp, CV = TRUE)
# create a table which compares the classifcation of the LDA model to the actual spp
ct.prod.norm <- table(Pac.EAA.frac.norm.zp$Fraction.grouped, LDA.Pac.EAA.frac.norm.zp$class)
# total percent of samples correctly classified is the sum of the diagonal of this table
#sum(diag(prop.table(ct.prod.norm))) #71% effective
# what % of each species is being correctly classified
#diag(prop.table(ct.prod.norm, 1)) # symbionts 100% classified, plankton 89%
# create a training lda function from the source data - use this to classify the coral hosts
training.EAA.norm.samples <- lda(Fraction.grouped ~ Ile.n + Leu.n + Lys.n + Phe.n + Thr.n + Val.n, data = Pac.EAA.frac.norm.zp)
#examine coefficents of the discriminants to determine AAs contributing to groups seperation
#training.EAA.norm.samples$scaling # most for Valine > Leucine > Isoleucine > Phenylalanine
# create a dataframe with these LDA coordinates
datPred.norm.zp <- data.frame(Fraction.grouped=Pac.EAA.frac.norm.zp$Fraction.grouped,
predict(training.EAA.norm.samples)$x) #create data.frame
datPred.norm.zp$ID<-Pac.EAA.norm.zp$ID
# predict the coral animal fractions based on ess
host.norm.res <- predict(training.EAA.norm.samples, Pac.EAA.frac.norm.ho) # 6 classified as plankton-POM
#save the predicted coordinates
datPred2.norm.ho <- data.frame(Fraction.grouped='animal', host.norm.res$x)
datPred2.norm.ho$ID<-Pac.EAA.norm.ho$ID
# add to the original animal dataframe which individual got classified as what:
Pac.EAA.norm.ho$class <- host.norm.res$class #--- only 2 classified as non-symb
# merge the source and coral animal dataframes for plotting
datPred3.norm.Pac <- rbind(datPred.norm.zp, datPred2.norm.ho)
#re-attach medata by merging with ID
# original df with palmyra corals and zoox dropped = "Pac.EAA.frac.df"
EAA.frac.LD.norm.meta<-merge(datPred3.norm.Pac, Pac.ess.norm.df, by="ID")
names(EAA.frac.LD.norm.meta)[names(EAA.frac.LD.norm.meta)=="Fraction.grouped.x"] <- "Fraction.grouped"
#remove redundant group
EAA.frac.LD.norm.meta<-EAA.frac.LD.norm.meta[,-8]
EAA.frac.LD.norm.meta$Fraction.source2<-EAA.frac.LD.norm.meta$Fraction.grouped
EAA.frac.LD.norm.meta$Fraction.source2<-revalue(EAA.frac.LD.norm.meta$Fraction.source2,
c("POM"="plankton"))
EAA.frac.LD.norm.meta<-droplevels(EAA.frac.LD.norm.meta)
# for ellipses, something weird about it loading relative to other packages
detach("package:ellipse", unload=TRUE)
library (ellipse)
dat_ell.norm.EAA <- data.frame()
for(g in levels(EAA.frac.LD.norm.meta$Fraction.source2)){
dat_ell.norm.EAA <- rbind(dat_ell.norm.EAA,
cbind(as.data.frame(with
(EAA.frac.LD.norm.meta[EAA.frac.LD.norm.meta$Fraction.source2==g,],
ellipse(cor(LD1, LD2),
scale=c(sd(LD1),sd(LD2)),
centre=c(mean(LD1),mean(LD2))))), Fraction.source2=g))
}
# no ellipse drawn for animal
dat_ell.norm.EAA.source<-dat_ell.norm.EAA[!(dat_ell.norm.EAA$Fraction.source2=="animal"),]
# add back Location column
EAA.frac.LD.norm.meta$Location<-Pac.ess.norm.df$Location
# order factor
EAA.frac.LD.norm.meta$Fraction.grouped<-factor(EAA.frac.LD.norm.meta$Fraction.grouped,
levels=c("animal", "symbiont", "plankton", "POM"))
# Fraction colors
frac.color<-c("coral", "springgreen3", "steelblue1", "skyblue4")
LDA.EAA.frac.norm<-ggplot(EAA.frac.LD.norm.meta, aes(LD1, LD2)) +
geom_point(aes(color = Fraction.grouped, shape=Location), size=2)+
scale_color_manual(values=frac.color) +
scale_shape_manual(values=c(1,16))+
geom_path(data=dat_ell.norm.EAA.source, aes(x=x,y=y,color=Fraction.source2),
size=0.5,linetype=2, show.legend=FALSE) +
theme(axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")),
axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(text = element_text(size=8)) +
theme(legend.text=element_text(size=10), legend.key = element_blank()) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black", size=0.5))
print(LDA.EAA.frac.norm)![*Figure 4a*. Linear discriminant analysis of carbon isotope fingerprints using mean-normalized values of six essential amino acids (isoleucine, leucine, lysine, phenylalanine, threonine, tyrosine, valine). Ellipses represent 95% confidence ellipses for each nutrition group (autotrophy [symbiont] or heterotrophy [plankton-POM). Hawaii data are host (n=6), symbiont (n=6) and plankton (n=1); Palmyra data are host (n = 19), symbionts (n=11), plankton (n=9) and POM (n=8) (Fox et al. 2019).](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAYAAAD0ZtPZAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAEAASURBVHgB7N0HfNT1/cfx9++SEMImEPZeMlSWoiguquLAWfdCa2tbR134ty7c22qts1rUuqiKdaDWhYKgKEO2bNk7hBEImXf/7/d3JJdAgDu4S+5+9/r2ceR3d9/fdzy/qUk+9x1OwCSREEAAAQQQQAABBBBAAAEEEEAAAQQQQAABDwr4PNgnuoQAAggggAACCCCAAAIIIIAAAggggAACCLgCBED5RkAAAQQQQAABBBBAAAEEEEAAAQQQQAABzwoQAPXs0NIxBBBAAAEEEEAAAQQQQAABBBBAAAEEECAAyvcAAggggAACCCCAAAIIIIAAAggggAACCHhWgACoZ4eWjiGAAAIIIIAAAggggAACCCCAAAIIIIAAAVC+BxBAAAEEEEAAAQQQQAABBBBAAAEEEEDAswIEQD07tHQMAQQQQAABBBBAAAEEEEAAAQQQQAABBAiA8j2AAAIIIIAAAggggAACCCCAAAIIIIAAAp4VIADq2aGlYwgggAACCCCAAAIIIIAAAggggAACCCBAAJTvAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwrAABUM8OLR1DAAEEEEAAAQQQQAABBBBAAAEEEEAAAQKgfA8ggAACCCCAAAIIIIAAAggggAACCCCAgGcFCIB6dmjpGAIIIIAAAggggAACCCCAAAIIIIAAAgikQoBAtAUKCgr05ptvusU2a9ZMp556arSroDwEEEAAAQQQQAABBBBAAAEEEEAAAQTCEnACJoWVk0wIhCmQnZ2trKwsN/eAAQM0bty4MO8kGwIIIIAAAggggAACCCCAAAIIIIAAAtEVYAl8dD0pDQEEEEAAAQQQQAABBBBAAAEEEEAAAQTiSIAAaBwNBk1BAAEEEEAAAQQQQAABBBBAAAEEEEAAgegKEACNrielIYAAAggggAACCCCAAAIIIIAAAggggEAcCRAAjaPBoCkIIIAAAggggAACCCCAAAIIIIAAAgggEF0BAqDR9aQ0BBBAAAEEEEAAAQQQQAABBBBAAAEEEIgjAQKgcTQYNAUBBBBAAAEEEEAAAQQQQAABBBBAAAEEoitAADS6npSGAAIIIIAAAggggAACCCCAAAIIIIAAAnEkQAA0jgaDpiCAAAIIIIAAAggggAACCCCAAAIIIIBAdAUIgEbXk9IQQAABBBBAAAEEEEAAAQQQQAABBBBAII4ECIDG0WDQFAQQQAABBBBAAAEEEEAAAQQQQAABBBCIrgAB0Oh6UhoCCCCAAAIIIIAAAggggAACCCCAAAIIxJEAAdA4GgyaggACCCCAAAIIIIAAAggggAACCCCAAALRFSAAGl1PSkMAAQQQQAABBBBAAAEEEEAAAQQQQACBOBIgABpHg0FTEEAAAQQQQAABBBBAAAEEEEAAAQQQQCC6AgRAo+tJaQgggAACCCCAAAIIIIAAAggggAACCCAQRwIEQONoMGgKAggggAACCCCAAAIIIIAAAggggAACCERXgABodD0pDQEEEEAAAQQQQAABBBBAAAEEEEAAAQTiSIAAaBwNBk1BAAEEEEAAAQQQQAABBBBAAAEEEEAAgegKEACNrielIYAAAggggAACCCCAAAIIIIAAAggggEAcCRAAjaPBoCkIIIAAAggggAACCCCAAAIIIIAAAgggEF0BAqDR9aQ0BBBAAAEEEEAAAQQQQAABBBBAAAEEEIgjAQKgcTQYNAUBBBBAAAEEEEAAAQQQQAABBBBAAAEEoitAADS6npSGAAIIIIAAAggggAACCCCAAAIIIIAAAnEkQAA0jgaDpiCAAAIIIIAAAggggAACCCCAAAIIIIBAdAUIgEbXk9IQQAABBBBAAAEEEEAAAQQQQAABBBBAII4ECIDG0WDQFAQQQAABBBBAAAEEEEAAAQQQQAABBBCIrgAB0Oh6UhoCCCCAAAIIIIAAAggggAACCCCAAAIIxJFAahy1haYggAACCCCwi0Bg7hQFJn+jwPIF0tbNUp36ctoeIKfvQDkH9N4lPy8ggAACCCCAAAIIIIAAAgggUF6AAGh5Da4RQAABBOJGILA5R/7XHpTm/VyxTRvXucHQwPhPpG6HyDfkNjn1Mivm4RkCCCCAAAIIIIAAAggggAACOwQIgPKtgAACCCAQdwKBnHXyP3GNtClbatpazqCL5HQ7VKrbQMrdpMDsiQp8OUKaM1n+R/8s3y3PyWnQOO76QYMQQAABBBBAAAEEEEAAAQSqX4A9QKt/DGgBAggggEA5gYDfL//Lw9zgp3PIb+S7/V/yHX6SnPqN5PhS3K++I06W745/yelzrGRmhPpfvluBQKBcKVwigAACCCCAAAIIIIAAAgggEBQgAMp3AgIIIIBAXAkEJo2Wls6T2nWTM+SvctJqVNo++7pz+e1Smy7S4l8UmPJtpfl4EQEEEEAAAQQQQAABBBBAILkFCIAm9/jTewQQQCDuBAI/fe62yXfG7+Wk7HmnFic1Tb7Tr3TzB34M3hd3HaJBCCCAAAIIIIAAAggggAAC1SpAALRa+akcAQQQQGAXgYUzpZq1pc69dnmr0hcO6Cul15QWmftICCCAAAIIIIAAAggggAACCOwkQAB0JxCeIoAAAghUn0Bg+1apuEhqmGX2+wzvR5STkiLVz5IK8hUozK++xlMzAggggAACCCCAAAIIIIBAXAqE99dlXDadRiGAAAIIeE6gRobkOJINhEaS8k1+GzBNS4/kLvIigAACCCCAAAIIIIAAAggkgQAB0CQYZLqIAAIIJIqAO5uzaWv3BPhA9uqwmh1Yt0LaslFq1tbETk3wlIQAAggggAACCCCAAAIIIIBAOQECoOUwuEQAAQQQqH4Bp9fRbiMCX78bVmMCX7/j5nN6B+8L6yYyIYAAAggggAACCCCAAAIIJI0AAdCkGWo6igACCCSGgDPwHPcQpMB3HyowdeweG+2f/I0C4z+RatWRc9xv95iXNxFAAAEEEEAAAQQQQAABBJJTgABoco47vUYAAQTiVsCpU1++y2512+f/133yfzxcgfy8Cu0NbN8m/0cvK/DqA+6eob7LbpNTq26FPDxBAAEEEEAAAQQQQAABBBBAwAo4AZOgQCCaAtnZ2crKMicymzRgwACNGzcumsVTFgIIJImAO7vzjUelokIpNU3q0ENO3YYKbMmRFv8SPC3eHHrkG2KCn32OSRIVuokAAggggAACCCCAAAIIIBCpQGqkN5AfAQQQQACBqhDwHTJQgfbdFfjsdbMU/jtp/jSVfWKXUVtOvxPknHKZnMymVdEc6kAAAQQQQAABBBBAAAEEEEhQAQKgCTpwNBsBBBBIBgGnUTM5l/6fAhfdJK1fJW3bLJkl8mrcUu6J8cmAQB8RQAABBBBAAAEEEEAAAQT2S4AA6H7xcTMCCCCAQFUIOCnmx1WzNlVRFXUggAACCCCAAAIIIIAAAgh4TIBDkDw2oHQHAQQQQAABBBBAAAEEEEAAAQQQQAABBEICBEBDFlwhgAACCCCAAAIIIIAAAggggAACCC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)
Figure 4a. Linear discriminant analysis of carbon isotope fingerprints using mean-normalized values of six essential amino acids (isoleucine, leucine, lysine, phenylalanine, threonine, tyrosine, valine). Ellipses represent 95% confidence ellipses for each nutrition group (autotrophy [symbiont] or heterotrophy [plankton-POM). Hawaii data are host (n=6), symbiont (n=6) and plankton (n=1); Palmyra data are host (n = 19), symbionts (n=11), plankton (n=9) and POM (n=8) (Fox et al. 2019).
ggsave("figures/Fig 4a. LDA analysis.pdf", height=5, width=5)########################################################################
#########################################################################
### Raw data = 'Pacific.RedSea.AA'
# LDA datasets:
### Palmyra-- POM + plankton, coral and symbiont
### Hawaii-- plankton, coral, symbiont
### RedSea-- plankton and coral holobiont
Pacific.RedSea.AA<-read.csv("data/Pacific.Redsea.EAA_full.df.csv")
# 5 EAA
Pac.Red.EAA.df<- Pacific.RedSea.AA %>%
select(Location, Species, Fraction, Leu, Ile, Phe, Thr, Val)
###### ###### ###### ###### ######
###### NORMALIZING AAESS DATA BY Sample MEAN (mean of all AA d13C) - raw individual AAd13C value
# raw compiled data is 'Pac.Red.EAA.df'
Pac.Red.ess.norm<-Pac.Red.EAA.df
#make ID column to run the normalization
Pac.Red.ess.norm$ID<-1:nrow(Pac.Red.ess.norm)
for(i in 1:length(Pac.Red.ess.norm$ID)){
Pac.Red.ess.norm$Leu.n[i] <- (Pac.Red.ess.norm$Leu[i]-mean(as.numeric(Pac.Red.ess.norm[i,4:8])))
Pac.Red.ess.norm$Ile.n[i] <- (Pac.Red.ess.norm$Ile[i]-mean(as.numeric(Pac.Red.ess.norm[i,4:8])))
Pac.Red.ess.norm$Phe.n[i] <- (Pac.Red.ess.norm$Phe[i]-mean(as.numeric(Pac.Red.ess.norm[i,4:8])))
Pac.Red.ess.norm$Thr.n[i] <- (Pac.Red.ess.norm$Thr[i]-mean(as.numeric(Pac.Red.ess.norm[i,4:8])))
Pac.Red.ess.norm$Val.n[i] <- (Pac.Red.ess.norm$Val[i]-mean(as.numeric(Pac.Red.ess.norm[i,4:8])))
}
Pac.Red.ess.norm<-Pac.Red.ess.norm[,c(1:3,9:14)] # drop raw columns and reorder
###############
############### Normalized LDA data is 'Pac.Red.ess.norm.df'
# add new column of factors
Pac.Red.ess.norm$Fraction.grouped<-Pac.Red.ess.norm$Fraction
#rename these factors to animal, symbiont, plankton, POM
Pac.Red.ess.norm$Fraction.grouped<-revalue(Pac.Red.ess.norm$Fraction.grouped,
c("host.Pal"="animal", "host.HI"="animal",
"symb.Pal"="symbiont", "symb.HI"="symbiont",
"plank.Pal"="plankton", "plank.HI"="plankton",
"plank.redsea"="plankton", "coral.holob"= "holobiont",
"POM.Pal"="POM"))
#reorder
Pac.Red.ess.norm.df<-Pac.Red.ess.norm[,c(1:4,10,5:9)]
# separate out animal
# plankton, POM, symb = "zp"
Pac.Red.EAA.norm.zp<-
Pac.Red.ess.norm.df[!(Pac.Red.ess.norm.df$Fraction.grouped=="animal" | Pac.Red.ess.norm.df$Fraction.grouped=="holobiont"),]
# host animal = "ho"
Pac.Red.EAA.norm.ho<-
Pac.Red.ess.norm.df[(Pac.Red.ess.norm.df$Fraction.grouped=="animal" | Pac.Red.ess.norm.df$Fraction.grouped=="holobiont"),]
# drop factor levels
Pac.Red.EAA.norm.zp<-droplevels(Pac.Red.EAA.norm.zp)
Pac.Red.EAA.norm.ho<-droplevels(Pac.Red.EAA.norm.ho)
# remove ID columns for now
Pac.Red.EAA.frac.norm.zp<-Pac.Red.EAA.norm.zp[,c(-1:-4)]
Pac.Red.EAA.frac.norm.ho<-Pac.Red.EAA.norm.ho[,c(-1:-4)]
##########################################################################
############ training for leave one out: how well can we estimate the sources?
# with CV = true, results for classes and posteriors are for cross validation (CV, leave one out)
LDA.Pac.Red.EAA.frac.norm.zp <- lda(Fraction.grouped ~ Ile.n + Leu.n + Phe.n + Thr.n + Val.n, data = Pac.Red.EAA.frac.norm.zp, CV = TRUE)
# create a table which compares the classifcation of the LDA model to the actual spp
ct.prod.norm.PR <- table(Pac.Red.EAA.frac.norm.zp$Fraction.grouped, LDA.Pac.Red.EAA.frac.norm.zp$class)
# total percent of samples correctly classified is the sum of the diagonal of this table
#sum(diag(prop.table(ct.prod.norm.PR))) #83% effective
# what % of each species is being correctly classified
#diag(prop.table(ct.prod.norm.PR, 1)) # symbionts 100% classified, plankton 94%, POM as the plankton
# create a training lda function from the source data - use this to classify the coral hosts
training.EAA.norm.samples.PR <- lda(Fraction.grouped ~ Ile.n + Leu.n + + Phe.n + Thr.n + Val.n, data = Pac.Red.EAA.frac.norm.zp)
#examine coefficents of the discriminants to determine AAs contributing to groups seperation
#training.EAA.norm.samples.PR$scaling # most for Leucine > Phenylalanine >Isoleucine
# create a dataframe with these LDA coordinates
datPred.norm.zp.PR <- data.frame(Fraction.grouped=Pac.Red.EAA.frac.norm.zp$Fraction.grouped,
predict(training.EAA.norm.samples.PR)$x) #create data.frame
datPred.norm.zp.PR$ID<-Pac.Red.EAA.norm.zp$ID
# predict the coral animal fractions based on ess
host.norm.res.PR <- predict(training.EAA.norm.samples.PR, Pac.Red.EAA.frac.norm.ho) # 6 classified as plankton-POM
#save the predicted coordinates
datPred2.norm.ho.PR <- data.frame(Fraction.grouped='animal', host.norm.res.PR$x)
datPred2.norm.ho.PR$ID<-Pac.Red.EAA.norm.ho$ID
# add to the original animal dataframe which individual got classified as what:
Pac.Red.EAA.norm.ho$class <- host.norm.res.PR$class #--- only 6 classified as non-symb or host
group.host<-cbind(datPred2.norm.ho.PR, host.norm.res.PR$class)
group.host<-group.host[,c(1,4,5)]
# merge the source and coral animal dataframes for plotting
datPred3.norm.PR <- rbind(datPred.norm.zp.PR, datPred2.norm.ho.PR)
#re-attach metadata by merging with ID
EAA.frac.LD.norm.meta.PR<-merge(datPred3.norm.PR, Pac.Red.ess.norm.df, by="ID")
# check group membership with metadata
group.memb<-merge(EAA.frac.LD.norm.meta.PR, group.host, by="ID")
group.memb$foodsource<-ifelse(group.memb$`host.norm.res.PR$class`=="symbiont", TRUE, FALSE)
names(EAA.frac.LD.norm.meta.PR)[names(EAA.frac.LD.norm.meta.PR)=="Fraction.grouped.x"] <- "Fraction.grouped"
#remove redundant group
EAA.frac.LD.norm.meta.PR<-EAA.frac.LD.norm.meta.PR[,-2]
names(EAA.frac.LD.norm.meta.PR)[names(EAA.frac.LD.norm.meta.PR)=="Fraction.grouped.y"] <- "Fraction.grouped"
###### revalue to match other figures
EAA.frac.LD.norm.meta.PR$Fraction.grouped<-revalue(EAA.frac.LD.norm.meta.PR$Fraction.grouped,
c("animal"="host"))
# order factors
EAA.frac.LD.norm.meta.PR$Fraction.grouped<-factor(EAA.frac.LD.norm.meta.PR$Fraction.grouped,
levels=c("holobiont", "host", "symbiont", "plankton", "POM"))
EAA.frac.LD.norm.meta.PR$Location<-factor(EAA.frac.LD.norm.meta.PR$Location,
levels=c("Oahu", "Palmyra", "Red Sea"))
# make new factor level for ellipses
EAA.frac.LD.norm.meta.PR$Fraction.source2<-EAA.frac.LD.norm.meta.PR$Fraction.grouped
EAA.frac.LD.norm.meta.PR$Fraction.source2<-revalue(EAA.frac.LD.norm.meta.PR$Fraction.source2,
c("POM"="plankton"))
EAA.frac.LD.norm.meta.PR$Fraction.source2<- droplevels(EAA.frac.LD.norm.meta.PR$Fraction.source2)
# for ellipses
dat_ell.norm.EAA.PR <- data.frame()
for(g in levels(EAA.frac.LD.norm.meta.PR$Fraction.source2)){
dat_ell.norm.EAA.PR <- rbind(dat_ell.norm.EAA.PR,
cbind(as.data.frame(with
(EAA.frac.LD.norm.meta.PR[EAA.frac.LD.norm.meta.PR$Fraction.source2==g,],
ellipse(cor(LD1, LD2),
scale=c(sd(LD1),sd(LD2)),
centre=c(mean(LD1),mean(LD2))))), Fraction.source2=g))
}
# Fraction colors
frac.color<-c("orchid", "coral", "springgreen3", "steelblue1", "skyblue4")
EAA.frac.LD.norm.meta.PR$Location<-revalue(EAA.frac.LD.norm.meta.PR$Location,
c("Oahu"="Hawaii"))
LDA.EAA.frac.norm.PR<-ggplot(EAA.frac.LD.norm.meta.PR, aes(LD1, LD2)) +
geom_point(aes(color = Fraction.grouped, shape=Location), size=2)+
scale_color_manual(values=frac.color) +
scale_shape_manual(values=c(16,1,22))+
geom_path(data=dat_ell.norm.EAA.PR, aes(x=x,y=y,color=Fraction.source2),
size=0.5,linetype=2, show.legend=FALSE) +
theme(axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")),
axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(text = element_text(size=8)) +
theme(legend.text=element_text(size=10), legend.key = element_blank(), legend.title = element_blank()) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black", size=0.5))
#LDA plot
print(LDA.EAA.frac.norm.PR)![*Figure 4b*. Linear discriminant analysis of carbon isotope fingerprints of corals, symbionts, and plankton in Palmyra, Hawai'i, and Red Sea using mean-normalized values of five essential amino acids (isoleucine, leucine, phenylalanine, threonine, tyrosine, valine). Ellipses represent 95% confidence ellipses for each nutrition group (autotrophy [symbiont] or heterotrophy [plankton-POM).](data:image/png;base64,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)
Figure 4b. Linear discriminant analysis of carbon isotope fingerprints of corals, symbionts, and plankton in Palmyra, Hawai’i, and Red Sea using mean-normalized values of five essential amino acids (isoleucine, leucine, phenylalanine, threonine, tyrosine, valine). Ellipses represent 95% confidence ellipses for each nutrition group (autotrophy [symbiont] or heterotrophy [plankton-POM).
ggsave("figures/Fig 4b. LDA.PacRedSea.pdf", height=5, width=5)
##############################################################Compare the mean values of d13C-AA for samples of plankton and POM in Palmyra (Fox et al, 2019) with those of our study. This is Figure S4
# summary of means, pretty close
library(dplyr)
plank.meta<-Pacific.AA[(Pacific.AA$Fraction=="plank.HI" |
Pacific.AA$Fraction=="plank.Pal" |
Pacific.AA$Fraction=="POM.Pal"),]
plank.meta<-plank.meta[,c(-1:-2)] # remove extra columns
AA.means.mean<-plank.meta %>%
na.omit() %>%
group_by(Fraction) %>%
summarise_all("mean")
# melt column data to rows
md <- melt(plank.meta, id=("Fraction"))
colnames(md)<-c("Fraction", "AA", "d13C.AA")
md$Fraction<-droplevels(md$Fraction)
plank.metadf<-md
# drop levels to just Palmyra and Hawaii
plank.metadf$Fraction<-revalue(plank.metadf$Fraction, c("plank.Pal"="Plankton Palmyra", "POM.Pal"="POM Palmyra", "plank.HI" = "Plankton Hawaii"))
df.mean.pl<-aggregate(d13C.AA~Fraction+AA, data=plank.metadf, mean, na.rm=TRUE)
df.SE.pl<-aggregate(d13C.AA~Fraction+AA, data=plank.metadf, na.rm=TRUE, std.error)
df.n.pl<-aggregate(d13C.AA~Fraction+AA, data=plank.metadf, length)
df.SE.pl[is.na(df.SE.pl)] <- 0
colnames(df.SE.pl)[3]="SE"
df.mean.pl<-cbind(df.mean.pl, df.SE.pl[3])
df.mean.pl$AA<-factor(df.mean.pl$AA, levels=c("Ala","Asp", "Glx", "Ile", "Leu", "Pro", "Val", "Gly", "Lys", "Ser", "Phe", "Thr", "Tyr"))
pd <- position_dodge(0.5) #offset for error bars
d13C.plank.POM.meta<-ggplot(df.mean.pl, aes(x=AA, y=d13C.AA, group=Fraction, shape=Fraction)) +
geom_errorbar(aes(ymin=d13C.AA-SE, ymax=d13C.AA+SE, color=Fraction), size=.5, width=0, position=pd) +
geom_point(aes(color=Fraction), size=2, position=pd) +
ggtitle(expression(paste(delta^{13}, C[AA], " plankton"))) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Nonessential-AA", x=4, y=2.5, color="gray40") +
annotate(geom="text", label="Essential-AA", x=10, y=2.5, color="gray40") +
coord_cartesian(ylim=c(-35, 3)) +
xlab("Amino Acids") +
scale_color_manual(values=c("lightgoldenrod4", "mediumorchid", "darkolivegreen3")) +
scale_shape_manual(values=c(19,3,19))+
ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)"))) +
Fig.formatting
print(d13C.plank.POM.meta)
Figure S4. Comparison of δ13C values for non-essential and essential amino acids in a pooled plankton sample from Kāne‘ohe Bay, O‘ahu, Hawai‘i and plankton and POM from Palmyra (reported in (Fox et al. 2019)). Values are mean ± SE (n=6 - 9), except for the Hawai‘i plankton (n=1).
ggsave("figures/Fig S4. Pacific.plank.means.pdf", height=5, width=6, encod="MacRoman")Using normalized five essential amino acids, generate PCA plots for microalgae in culture, plankton, POM. Then make a new PCA to include Symbiodiniaceae and compare these sources.
# Compare the plankton and cultured algae
### Raw data = 'Pacif_RedS_Aloha_source_AAd13C'
### Palmyra-- POM + plankton
### Hawaii-- plankton
### RedSea-- plankton
### Station Aloha plankton
### Larsen cultured microalgae
Algae_plank.AA<-read.csv("data/Pacif_RedS_Aloha_source_AAd13C.csv")
#rename columns
Algae_plank.AA<- plyr::rename(Algae_plank.AA, c("Ala13C"="Ala", "Gly13C"="Gly", "Thr13C" = "Thr",
"Ser13C" = "Ser", "Val13C" = "Val", "Leu13C" = "Leu",
"Ile13C" = "Ile", "Pro13C"= "Pro", "Asp13C" = "Asp",
"Glx13C" = "Glx", "Phe13C" = "Phe", "Tyr13C" = "Tyr", "Lys13C" = "Lys"))
#drop coral animal and holobiont
Algae_plank.AA<-Algae_plank.AA[!(Algae_plank.AA$Fraction=="Animal" | Algae_plank.AA$Fraction=="Holobiont"),]
Algae_plank.AA<-droplevels(Algae_plank.AA)
# select the 5 essential amino acids found in all samples
# 5 EAA
Algae_plank.AA.df<- Algae_plank.AA %>%
dplyr::select(Location, Fraction, Leu, Ile, Phe, Thr, Val)
###### ###### ###### ###### ######
###### NORMALIZING AAESS DATA BY Sample MEAN (mean of all AA d13C) - raw individual AAd13C value
# raw compiled data is 'Algae_plank.AA.df'
Algae_plank.ess.norm<-Algae_plank.AA.df
#make ID column to run the normalization
Algae_plank.ess.norm$ID<-1:nrow(Algae_plank.ess.norm)
for(i in 1:length(Algae_plank.ess.norm$ID)){
Algae_plank.ess.norm$Leu.n[i] <- (Algae_plank.ess.norm$Leu[i]-mean(as.numeric(Algae_plank.ess.norm[i,3:7])))
Algae_plank.ess.norm$Ile.n[i] <- (Algae_plank.ess.norm$Ile[i]-mean(as.numeric(Algae_plank.ess.norm[i,3:7])))
Algae_plank.ess.norm$Phe.n[i] <- (Algae_plank.ess.norm$Phe[i]-mean(as.numeric(Algae_plank.ess.norm[i,3:7])))
Algae_plank.ess.norm$Thr.n[i] <- (Algae_plank.ess.norm$Thr[i]-mean(as.numeric(Algae_plank.ess.norm[i,3:7])))
Algae_plank.ess.norm$Val.n[i] <- (Algae_plank.ess.norm$Val[i]-mean(as.numeric(Algae_plank.ess.norm[i,3:7])))
}
Algae_plank.ess.norm<-Algae_plank.ess.norm[,c(1:2,8:13)] # drop raw columns and reorder
Algae_plank.ess.norm$Location<-factor(Algae_plank.ess.norm$Location,
levels=c("Oahu", "Palmyra", "Red Sea", "Station Aloha", "culture"))
# to match other figures
Algae_plank.ess.norm$Location<-revalue(Algae_plank.ess.norm$Location, c("Oahu"="Hawaii"))
##############################################################
######## PCA
##############################################################
# Algae_plank.ess.norm
# culured algae, plankton or POM
##############################################################
##############################################################
# remove symbiont and look at plankton and microalgae
Algae_plank.ess.norm.nosymb<-Algae_plank.ess.norm[!(Algae_plank.ess.norm$Fraction=="Symbiont"),]
Algae_plank.ess.norm.nosymb<-droplevels(Algae_plank.ess.norm.nosymb)
PC.plank<- prcomp(Algae_plank.ess.norm.nosymb[,c(-1:-3)], center = TRUE, scale= TRUE)
PC.plank.summary<-summary(PC.plank)
newdat.plank<-PC.plank$x[,1:4]
#plot(PC.plank, type="lines", main="PC.area eigenvalues")
PCA.col2<-c("palevioletred3", "dodgerblue", "goldenrod")
#Algae_plank.ess.norm.nosymb$Fraction<-revalue(Algae_plank.ess.norm.nosymb$Fraction, c("POM"="Plankton"))
##############################################################
## PC1 and PC2
PCA.plank <- ggbiplot(PC.plank, choices = 1:2, obs.scale = 1, var.scale = 1,
groups=Algae_plank.ess.norm.nosymb$Fraction,
ellipse = TRUE, circle = FALSE, alpha=0, ellipse.prob=0.90) + #alpha = zero makes points clear
geom_point(aes(colour=Algae_plank.ess.norm.nosymb$Fraction,
shape=Algae_plank.ess.norm.nosymb$Location), size = 2) +
scale_x_continuous(breaks=pretty_breaks(n=5))+
scale_color_manual(values=PCA.col2)+
scale_shape_manual(values=c(16,1,22,2,3))+
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme_classic() +
theme(legend.text=element_text(size=10)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.title = element_blank())+
theme(aspect.ratio=0.8)
##############################################################
#PCA plot
#print(PCA.plank)
#ggsave("figures/Fig S5a. PCA.EAAn.plank.pdf", height=5, width=5)
##############################################################
##############################################################
######## With symbionts, recreate same analysis
PC.plk.sym<- prcomp(Algae_plank.ess.norm[,c(-1:-3)], center = TRUE, scale= TRUE)
PC.plk.sym.summary<-summary(PC.plk.sym)
newdat.plk.sym<-PC.plk.sym$x[,1:4]
#plot(PC.plk.sym, type="lines", main="PC.area eigenvalues")
PCA.col.4<-c("palevioletred3", "dodgerblue", "goldenrod", "mediumseagreen")
## PC1 and PC2
PCA.plank.sym <- ggbiplot(PC.plk.sym, choices = 1:2, obs.scale = 1, var.scale = 1,
groups=Algae_plank.ess.norm$Fraction,
ellipse = TRUE, circle = FALSE, alpha=0, ellipse.prob=0.90) + #alpha = zero makes points clear
geom_point(aes(colour=Algae_plank.ess.norm$Fraction,
shape=Algae_plank.ess.norm$Location), size = 2) +
scale_x_continuous(breaks=pretty_breaks(n=5))+
scale_color_manual(values=PCA.col.4)+
scale_shape_manual(values=c(16,1,22,2,3))+
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme_classic() +
theme(legend.text=element_text(size=10)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.title = element_blank())+
theme(aspect.ratio=0.8)
#PCA plot
#print(PCA.plank.sym)
#ggsave("figures/Fig S5b. PCA.EAAn.plan.alg.symb.pdf", height=5, width=5)
##############################################################
#########
# edit plots to make a combined single column plot
### make plots with only colors for sample types (fractions)
PCA.plank.mod<-PCA.plank + guides(shape = FALSE) + theme(legend.direction = 'horizontal', legend.position = 'top')
PCA.plank.sym.mod<-PCA.plank.sym + guides(shape = FALSE) +
theme(legend.direction = 'horizontal', legend.position = 'top') +
annotate("text", x=3, y=3, size=5, label= expression(paste(delta^{13},C[EAA])))
# get the legend for location only
PCA.plank.sym.mod2<-PCA.plank.sym + guides(color = FALSE) + theme(legend.text=element_text(size=8))
leg.PCAs <- get_legend(
PCA.plank.sym.mod2 +theme(legend.box.margin = margin(0, 0, 0, 12)))
#### plot together
PCAs.supp<-plot_grid((PCA.plank.mod +
annotate(geom="text", size=3, label="(a)", x=-4.3, y=2.8, color="black")),
(PCA.plank.sym.mod +
annotate(geom="text", size=3, label="(b)", x=-4, y=3.4, color="black")),
leg.PCAs, rel_widths = c(3.5, 3.5, 1), ncol=3, nrow=1)
#PCA plot
print(PCAs.supp)
Figure 5a,b. Principal component (PC) analyses of mean-normalized essential amino acid δ13C (δ13CEAA) values in cultured microalgae, coastal and oceanic plankton and particulate organic matter (POM), and Symbiodiniaceae symbionts isolated from two coral species. Data matrixes in (a) and (b) are identical, except for the inclusion of symbiont samples in (b). Ellipses represent 90 % standard deviation with arrows for individual mean-normalized amino acids being significant (p<0.05) correlation vectors. Data from Hawai‘i (this study), Palmyra (Fox et al. 2019), the Red Sea (McMahon et al. 2015), Station Aloha (Hannides et al. 2008), and cultures (Larsen et al. 2013).
ggsave("figures/Fig S5comb. PCA.EAAn.plan.alg.symb.pdf", height=5, width=9)